Kaitlyn’s notebook: Geoduck hemolymph qPCR

qPCR was performed on samples with successful RNA extractions (by me and Sam) and cDNA ((by me and Sam) with primers designed by Sam according to the Roberts lab SOP. Here is a list of who extracted and RTed which sample:

  • Kaitlyn: 1, 2, 19, 27, 59, 62, 66, 11/15 Chewy, 11/15 Star, 11/21 Chewy, 11/21 Star
  • Sam:  57, 61, 39, 31

Here is my qPCR master mix calculations. Sam previously ran a qPCR using the above samples. All qPCR reactions were run in duplicate.

Conclusions

I achieved the same results as the previous qPCR run by Sam on his samples. A total of 2 known males and 2 known females amplified. Males had less VTG present based on the CF value (later = less amplification) compared to females. The later stage female had about 5 fold less VTG present. However, the amplification in other samples did not reach thresholds or occur.

The primers were designed base don an older geoduck genome, therefore we want to redesign primers with the most recent genome. We want to explore all selected targets identified in Emma’s paper to explore other possible proteins that may be a better match.

Kaitlyn’s notebook: Geoduck hemolymph Reverse transcription (cDNA)

I made cDNA from samples I had successfully extracted RNA from (1, 2, 19, 27, 59, 62, 66, 11/15 Chewy, 11/15 Star, 11/21 Chewy, 11/21 Star).

Reverse transcription was performed using 50ng of each sample with M-MLV Reverse Transcriptase from Promega accroding to the Roberts Lab SOP. Calculations can be found here. A 1:1000 dilution of primer was made to increase pipetting volume.

Samples were initially heat to 45C for 5 min for denaturation instead of 70C, however the incubation was redone immediately after at 70C. This would not impact the cDNA because the step only serves to denature the RNA to maximize primer binding.

Samples are stored in the -20C fridge in 209 in 20190107 Kaitlyn’s Box. Additionally, the hemolymph cDNA created by Sam is also located in the same box.

 

Kaitlyn’s notebook: Hemolymph RNA extractions 2

RNA was isolated with a Quick-DNA/RNA Microprep Plus Kit by ZymoResearch. The samples are stored in a box in the -80C freezer in 3, 3, 2, labelled “RNA isolations; geoduck 12/17”. Isolations were done according according to the manufacturer’s protocol on the following samples:

G-32 H
G-47 H
G-53 H
G-59 H
G-62 H
G-63 H
G-29 H
G-19 H
G-22 H
G-64 H
G-66 H
G-40 H
G-58 H
G-50 H

For hemolymph, 600ul of sample was taken and 2400ul of lysis buffer was added for prep. If 600ul of sample was not available, all of the sample was taken. The on-column DNase step was done, and the elution volume was 15ul.

Several columns were getting clogged from tissue in the sample.

Samples were quantified with the hsRNA Assay for Qubit according to manufacturer’s protocol. 1ul of sample was used and 199ul of working solution was used in each assay tube. All values were too low for quantification which was lickely caused by overloading the column, column clogging, and/or unknown technical error.

Grace’s Notebook: Attempt to concentrate test pools using Zymo RNA Clean and Concentrator Kit -5

Received the Zymo RNA Clean and Concentrator Kit -5 yesterday. Plan is to concentrate my 6 pooled samples. I tested it out on some test pools I made quickly. Short answer: unclear if it worked because the initial qubit RNA HS readings of the test pools were both “TOO LOW”… and after concentrating, one test pool was still “TOO LOW”, while the other was 2.20 ng/ ul in a 2 ul sample from RNA eluted in 35ul. Details in post. (Also at the end of the post: general updates on crab project and my January plans).

Creating test pools

Test Pool 1

FRP Uniq_ID sample_day infection_status maturity tube_number sample vol remaining total RNA ng left ng RNA for pool vol for pool total pool conc
6272 6272_111_9 9 0 I 111 13 260 30 1.5 120
6176 6176_48_9 9 0 M 48 13 40.3 30 9.677419355 120
6179 6179_34_9 9 0 M 34 13 101.92 30 3.826530612 120
6205 6205_121_9 9 0 I 121 13 80.99 30 4.81540931 120

total pool vol ul 12.11767753

water to add ul 42.88232247

Test Pool 2

FRP Uniq_ID sample_day infection_status maturity tube_number sample vol remaining total RNA ng left ng RNA for pool vol for pool total pool conc
6140 6140_8_9 9 1 I 8 13 208 30 1.875 120
6125 6125_144_9 9 1 I 144 13 185.9 30 2.097902098 120
6137 6137_81_9 9 1 I 81 13 403 30 0.967741935 120
6158 6158_101_9 9 1 I 101 13 54.34 30 7.177033493 120

total pool vol ul 19.81935928

water to add ul 35.18064072

Steps for Pooling

  1. Vortex each tube
  2. Pool the volume (vol for pool column) into tube
  3. Pipet to mix
  4. Add enough H20 to get to 55ul (Zymo RNA Clean and Concentrator kit requires samples to be at least 50ul)

Inital Qubit RNA HS on test pools

Ran 2ul of each pool on qubit using RNA HS Kit.

Both were “TOO LOW”.

This is becuase I SHOULD NOT have added so much water to the samples… I should have run them on qubit, THEN added enough water to get to at least 50ul sample…

Using the kit

img

Followed protocol. (used 2 of 10 preps)
Notes:
Step 2 – used 100% EtOH and centrifuge 10,000g 30 s
Step 5 – elute 35ul TE (NWGC requires at least 30ul and requires TE)

Post-concentration qubit

Ran 2ul of each 35ul concentrated test pool on qubit using RNA HS Kit.

Pool 1 –> “TOO LOW”
Pool 2 –> 2.20 ng in 2 ul (33 ul remaining) –> total RNA concentration remaining: 72.6 ng

If I pooled the samples well, they each should have had initially ~120 ng of RNA. However, since I added so much H20 before running the pools on the qubit, the RNA concentration was much too low to detect.

Summary

This probably works… I’m just a bit nervous to use it on my 6 pooled samples… maybe I’ll try again on a 2 new test pools and make sure I run 2ul on qubit before adding so much water for the concentrator kit…

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Shelly’s Notebook: Tues. Jan. 7, 2020 Geoduck methylation analysis on 5x cov. destranded CpG data

Steven generated destranded (“merged”) coverage files from Bismark .cov output.

Analysis below was done 12/20/2019 – 01/07/2020

Global methylation analysis

  • Using this jupyter notebook 20191222_GlobalMethylation_5x_CpGs.ipynb I created this table allc_5x_CpG.txt which has the following columns:
    1. Sample
    2. Number of mCs
    3. Number of total Cs
    4. % methylation
  • Using this R project 20191222.Rproj and this R markdown script Overall_CpG_analysis.Rmd I generated the following figures:
    • number of mCpGs across samples from different time points 5x_num_mCpG_boxplot.jpg
    • percent CpG methylation for each sample group 5xCovPercMeth_boxplots.jpg
    • percent CpG methyaltion for each sample group facetted by time 5xCovPercMeth_facet_boxplots.jpg
  • I used this R script CpG_analysis_d0to135.Rmd to generate this figure for percent CpG methylation for samples from day 0, day 10, and day 135 for PAG presentation: d0to135_5xCovPercMeth_boxplot.jpg

DMRs from 5x destranded coverage files

Convert 5x.tab files to Methylpy allc format

  • Used this jupyter notebook 20191222_DMRfind_5xmerg.ipynb to convert 5x.tab files to allc format on my account on Ostrich
    • previously attempted to do this on 12/20 using this jupyter notebook 20191220_DMRfind_5xmerg.ipynb but it turned out the last column in the 5x.tab file is in fact the number of unmethylated Cs AND NOT the total number of Cs so that is why I could not get DMRfind to work. See issue posted here.
    • copied new allc.tsv files to Gannet using same notebook 20191222_DMRfind_5xmerg.ipynb

Running Methylpy DMRfind for all 4 comparisons

Filter DMRs for coverage in 3/4 samples per group

Running group statistics (ANOVA) on DMRs

  • Using this script MCmax25_asinT_groupStats.Rmd I performed ANOVA on all the DMRs from each comparison.
    1. CHECK DATA DISTRIBUTION: First looked at each groups’ % methylation distribution
      • All ambient sampels: ambDMR_percmeth_hist.jpg
      • Day 10 samples: d10DMR_percmeth_hist.jpg
      • Day 135 samples: d135DMR_percmeth_hist.jpg
      • Day 145 samples: d145DMR_percmeth_hist.jpg
    2. TRANSFORM DATA: No distribution is normal so performed arcsin square root transformation and here’s how the distrubtions changed:
      • All ambient sampels: ambDMR_Tpercmeth_hist.jpg
      • Day 10 samples: d10DMR_Tpercmeth_hist.jpg
      • Day 135 samples: d135DMR_Tpercmeth_hist.jpg
      • Day 145 samples: d145DMR_Tpercmeth_hist.jpg
    3. Perform ANOVA on transformed data
    4. Plot % methylation x group for significant DMRs
      • DMRs significant at p value < 0.01
        • All ambient samples:
          • ambDMR_MCmax25_Taov0.01pHPercMeth.jpg
        • Day 10 samples:
          • d10DMR_MCmax25_Taov0.01pHPercMeth.jpg
        • Day 135 samples:
          • d135DMR_MCmax25_Taov0.01pHPercMeth.jpg
        • Day 145 samples:
          • d145DMR_MCmax25_Taov0.01pHPercMeth.jpg
      • DMRs significant at p value < 0.05
        • All ambient samples:
          • ambDMR_MCmax25_Taov0.05pHPercMeth.jpg
        • Day 10 samples:
          • d10DMR_MCmax25_Taov0.05pHPercMeth.jpg
        • Day 135 samples:
          • d135DMR_MCmax25_Taov0.05pHPercMeth.jpg
        • Day 145 samples:
          • d145DMR_MCmax25_Taov0.05pHPercMeth.jpg
    5. Plot heatmaps of DMRs x samples colored by % methylation
      • DMRs significant at p value < 0.01
        • All ambient samples: DMR_MCmax25DMR_Taov0.01_amb_heatmap.jpg
        • Day 10 samples: DMR_MCmax25DMR_Taov0.01_d10_heatmap.jpg
        • Day 135 samples: DMR_MCmax25DMR_Taov0.01_d135_heatmap.jpg
        • Day 145 samples: DMR_MCmax25DMR_Taov0.01_d145_heatmap.jpg
      • DMRs significant at p value < 0.05
        • All ambient samples: DMR_MCmax25DMR_Taov0.05_amb_heatmap.jpg
        • Day 10 samples: DMR_MCmax25DMR_Taov0.05_d10_heatmap.jpg
        • Day 135 samples: DMR_MCmax25DMR_Taov0.05_d135_heatmap.jpg
        • Day 145 samples: DMR_MCmax25DMR_Taov0.05_d145_heatmap.jpg
    6. Plot heatmaps of DMRs x group means colored by % methyaltion
      • DMRs significant at p value < 0.01
        • All ambient samples: DMR_MCmax25DMR_Taov0.01_ambmean_heatmap.jpg
        • Day 10 samples: DMR_MCmax25DMR_Taov0.01_d10mean_heatmap.jpg
        • Day 135 samples: DMR_MCmax25DMR_Taov0.01_d135mean_heatmap.jpg
        • Day 145 samples: DMR_MCmax25DMR_Taov0.01_d145mean_heatmap.jpg
      • DMRs significant at p value < 0.05
        • All ambient samples: DMR_MCmax25DMR_Taov0.05_ambmean_heatmap.jpg
        • Day 10 samples: DMR_MCmax25DMR_Taov0.05_d10mean_heatmap.jpg
        • Day 135 samples: DMR_MCmax25DMR_Taov0.05_d135mean_heatmap.jpg
        • Day 145 samples: DMR_MCmax25DMR_Taov0.05_d145mean_heatmap.jpg
    7. Identify persistent DMRs
      • Using this jupyter notebook 20191223_PersistantDMRs.ipynb I compared DMRs from day10 samples (aov_0.05pH_d10DMR.bed) and DMRs from day 135 samples (aov_0.05pH_d135DMR.bed)
      • none were overlapping and the closest DMR was > 16kb away.
      • realized the DMR may not be the same because all these analyses were done separately. If all samples were processed together, I could compare DMRs from different time points

Running Methylpy DMRfind on all samples together

Conclusions:

  • When I ran DMRfind on just the Day 10 samples and on just the Day 135 samples, then performed ANOVA on regions identified in each DMRfind run, there were no overlapping DMRs with significant pH effect (ANOVA p value < 0.05)
  • When I ran DMRfind on all 52 samples, then performed ANOVA on regions identified, 1 DMR (scaffold 3: 56511986-56512009) showing a significant pH effect (ANOVA pvalue < 0.05) was overlapping between day 10 and day 135 samples
  • ANOVA is not the best test to be using for this data because it is not normal
    • a GLM would likely be more sensitive but I would need to reformat the data to run this test (determine # Cs and # mCs for each region).
      • This is possible by running bedtools intersect or closest on DMR bedfile and counts (5x.tab) files https://osf.io/yem8n/files/ and then collapsing/summing the counts for DMRs. But I would need to code it.
  • For now, I’m just going to go with the results from running Methylpy DMRfind on all samples together.

Yaamini’s Notebook: January 2020 Goals

feels

I’m back from vacation and (theoretically) recharged and ready to start working again! I have grand plans for this month.

December Goals Recap:

Virginica Gonad Methylation:

  • SUBMITTED THAT PAPER (while on vacation RIP)!
  • Rejoiced.
  • Prepared metadata for BCO-DMO submission
  • Cleaned up repository and added relevant supplementary material. It still needs a bit of work but it was good enough for submission.

Gigas Gonad Methylation:

  • Did not figure out if more samples should be sequenced or attempt GO-MWU enrichment and DMG analysis…because paper and vacation.

Other:

  • Presented at Huxley Environmental Speaker Series! The students were really interested in the nitty-gritty of methylation analysis which was unexpected but nice. It was a good experience for me to figure out what information to include in talks to bulk it up, and what information isn’t as necessary for the story.
  • Wrote questions for the Marine Bio final. I got my evaluations back too and they were positive!
  • Did not finalize sample preparation flowchart for methylation analysis or figure out next steps for C. virginica sperm analysis…because paper and vacation.

January Goals

Gigas Gonad Methylation:

  • Attempt GO-MWU enrichment and DMG analysis to have better data
  • Figure out what additional samples need to be sequenced and how
  • Start putting together preliminary poster for ASLO

Virginica Sperm Methylation:

  • Meet with Katie and Alan to figure out what additional samples I have access to and what methods I should use

** All Mechanism Study**:

  • Figure out what samples I have access to and come up with an extraction and sequencing plan

Other:

  • Finalize C. virginica repository and post submitted paper on bioRXiv
  • Get committee reading lists and start studying for quals

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Grace’s Notebook: January 2020 Goals

Crab

  • Concentrate the 6 new pooled crab samples (recieved that sample kit of the RNA Clean and Concentrator -5 kit from Zymo – have a lot of time tomorrow to work on this)
  • Submit 6 new pooled samples (to NWGC because we have the quote from them)
  • Work on new results for AMSS presentation (my talk needs to be done by Monday, January 27th)

Oyster

  • FINISH PAPER –> just do it!

Other

  • Submit official MS thesis proposal (nearly completed)
  • Submit application for SAFS fellowship (have list of things to submit. nearly complete – just waiting on me finishing the MS thesis proposal and a few other easy tasks)
  • Figure out how to balance TA commitments with my thesis work and class work (taking Global Health 518- Understanding and Managing the Health Risks of Climate Change (human health))

Attend and present at AMSS 2020 January 27th – 30th

  • All my paperwork is in order. Waiting to register for conference until week before (Pam’s suggestion), and my talk slot is Tuesday, January 28th 9:15-9:30am.