Monday 8/10/15 and Tuesday 8/11/15

Monday 8/10/15

Larvae tanks

  • NF_Tank2_160 (224) -> 10,500 total: 500 for DNA, rest to NF_SetB
  • NF_Tank2_160 (100) -> swimmers only
  • HC_Tank2_160 (224) -> 9,187 total: 500 for DNA, 5,187 added to HC_SetB, rest to cultch set
  • HC_Tank2_160 (100) -> swimmers only
  • SS_Tank2_160 (224) -> 11,900 total, added to SS cultch
  • SS_Tank2_160 (100) -> swimmers only

Tile Set

  • Did live/dead counts for both A and B
  • Rinsed tiles (have been doing this everyday I’m in)
  • New totals
    • NF_SetB: 45,137
    • HC_SetB: 60,000

New larvae

  • no new larvae

Tuesday 8/11/15

Animal husbandry

  • Cleaned broodstock buckets
  • Rinsed cultch and tiles

Lab work

Extracted DNA from 24 larvae samples that would be good candidates for test 2b-RAD libraries. I chose sets of new larvae (“LC” for larvae catch), “160”s, and “224”‘s with 3-5 days in between.

Population Tank Family Size Date Storage Est. # Date extracted
1 Hood Canal LC >100 7/13/2015 75/95 EtOH 8/11/2015
2 Fidalgo Bay NA LC 100 7/13/2015 75% EtOH 8/11/2015
3 South Sound NA LC 100 7/13/2015 RNALater 8/11/2015
4 Hood Canal NA HC2 100 7/17/2015 RNALater 8/11/2015
5 Hood Canal NA LC 100 7/17/2015 RNALater 8/11/2015
6 South Sound NA LC 100 7/17/2015 RNALater 8/11/2015
7 Hood Canal NA LC 100 7/20/2015 RNALater 8/11/2015
8 Fidalgo Bay NA LC 100 7/20/2015 RNALater 8/11/2015
9 South Sound NA LC 100 7/20/2015 RNALater 8/11/2015
10 Hood Canal HC_Tank1_160 NA 160 7/20/2015 RNALater 8/11/2015
11 Fidalgo Bay NF_Tank1_new NA 160 7/20/2015 RNALater 8/11/2015
12 South Sound SS_Tank1_new NA 160 7/15/2015 RNALater 8/11/2015
13 Hood Canal HC_Tank1_new NA 160 7/24/2015 RNALater 8/11/2015
14 Fidalgo Bay NF_Tank1_new NA 160 7/24/2015 RNALater 8/11/2015
15 South Sound SS_Tank1_new NA 160 7/24/2015 RNALater 8/11/2015
16 Hood Canal HC_Tank1_new NA 160 7/27/2015 RNALater 8/11/2015
17 Fidalgo Bay NF_Tank1_new NA 160 7/27/2015 RNALater 8/11/2015
18 South Sound SS_Tank1_new NA 160 7/27/2015 RNALater 8/11/2015
19 Hood Canal HC_Tank2_160 224 8/3/2015 RNALater 8/11/2015
20 Fidalgo Bay NF_Tank2_160 224 8/3/2015 RNALater 8/11/2015
21 South Sound SS_Tank2_160 224 8/3/2015 RNALater 8/11/2015
22 Hood Canal HC_Tank2_160 >224 8/7/2015 RNALater 350 8/11/2015
23 Fidalgo Bay NF_Tank2_160 >224 8/7/2015 RNALater 504 8/11/2015
24 Oyster Bay SS_Tank2_160 >224 8/7/2015 RNALater 641 8/11/2015

I still have to go back over my notes to get estimated number for some of them. Storage was mostly in RNALater. A lot of the samples had some white precipitate on the bottom. If larvae weren’t  in the precipitate I sucked it out before adding the lysis buffer/proteinase K. Also had the same issue previously with larvae being buoyant in the RNALater even after a spindown. Halfway through trying to siphon off as much liquid as I could, I did some research and found that addition of some ice-cold PBS will change the density of the liquid and allow the larvae to settle out. This worked really well and was done for 2,10,13,15,19,20,21,24. It also dissolved most of the white precipitate.

Followed the protocol I listed here, with a 2.5 hour digest. Could not findd the gel rig set-up (found out later it’s in a different building).

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Thursday 7/30/15 (Lab work!)

I got to do some bona fide lab work today, which was a nice change of pace. I’ve been taking samples of larvae for DNA sequencing at various points throughout the experiment:

  • From all newly released larvae (either from each family or combined, depending on how I filtered them out)
  • From larvae in the “New” tanks that reach 160 microns
  • From larvae in the “160” tanks that reach 224 in size
  • Occasionally pooled larvae from a tank

These samples have mostly been stored at -20degC in .5-1 mL of RNALater, but duplicates of many were also stored in ethanol (1st in 75%, then in 95%). Earlier in the summer I wanted to do a test extraction to see if there was a particular storage method that worked best and figure out which extraction kit to use, but then the oysters needed maintenance 6 days a week and all of a sudden in was almost August. With the growth rate experiments and larval production essentially done, I finally had a day to do the test extraction.

Continue reading

Setting up an Oyster Garden

Monday (June 8) was my first day out at the NOAA Manchester Research Station in Washington State. Specifically, I’m working in the Kenneth K. Chew Center for Shellfish Research and Restoration. This shellfish hatchery is the result of collaboration between many groups and funding agencies, in particular the Puget Sound Restoration Fund (PSRF).

http://www.nwfsc.noaa.gov/news/features/hatchery/

The hatchery (right) and algae greenhouse (left)

My project this summer is to raise oysters descended from three Puget Sound populations under common conditions in order to measure differences in fitness. This type of experimental design is commonly referred to as a “common garden”, and allows one to control environmental variables so that phenotypic disparities among individuals can be attributed to their genetic differences. My fitness metrics are reproductive output, survivorship at different life stages, and growth rate. I will also be taking DNA/RNA samples along the way to see if mortality is random in respect to genotype, or due to purifying selection. With the RNA, I plan to look at differences in gene expression to help detect cryptic differences in phenotype between these populations.

Three source populations for common garden experiment

Three source populations for common garden experiment

This project is a collaboration with Steven Robert’s lab at the University of Washington, who previously conducted a reciprocal transplant experiment with offspring of wild oysters from these same populations. For that experiment, they outplanted the young oysters from each group at four different sites and measured growth rate, mortality, and reproductive characteristics. They observed significant variation at these metrics among populations and sites (informative slides and manuscript preprint available here). My experiment will be following up on these results by testing if population-level differences are consistent in a second generation under controlled environmental conditions.

As I’ve never raised shellfish before, this week has had a bit of a learning curve. Fortunately for me, the staff at the hatchery have been super helpful in showing me the ropes and advising on how to set up my experiment. I’m starting with about 100 adult oysters for each group (see lab notebook entries for data). These are the first generation (F1) offspring of wild oysters, and have been living in common conditions their entire lives- mostly hanging off the docks near the hatchery. Their offspring will be 2nd generation (F2) from the original broodstock, and should have any influence from maternal effects erased.

The adults were brought in to the hatchery on May 28 and placed in three separate buckets to avoid cross fertilization. To maximize genetic diversity and minimize the chance that one male fertilizes all of the females, I split each group into 5 buckets of ~20 oysters. These “families” will be marked, so that I can genotype them later and follow their offspring’s success throughout the experiment. Their water temperature was switched to a balmy 20°C this week, which will encourage them to start spawning and producing larvae.

(sorry for the lack of pictures, I’ll take some and put them up soon!)

Dec. 4, 2014

PCR of 16S Region

  • Verifying species identity as some may have actually been juvenile C. gigas
  1. BC3_7 redo
  2. BC3_11 redo
  3. BC3_12
  4. BC3_13
  5. BC3_15
  6. BC3_16
  7. BC3_17
  8. BC3_18
  9. OR2_3
  10. OR2_4
  11. OR2_5
  12. OR2_6
  13. OR2_7
  14. OR2_8
  15. OR2_9
  16. OR2_10
  17. OR2_11
  18. OR2_12
  19. OR2_15
  20. OR2_16
  21. OR2_17
  22. OR2_18
  23. OR2_19
  24. OR2_20
  25. OR3_1
  26. OR3_2
  27. OR3_3
  28. OR3_5
  29. OR3_6
  30. OR3_9
  31. OR3_10
  32. OR3_12
  33. OR3_13
  34. OR3_15
  35. OR3_17
  36. OR3_18
  37. WA9_2
  38. WA9_3
  39. WA9_4
  40. WA9_5
  41. WA9_6
  42. WA9_7
  43. WA9_8
  44. WA9_9
  45. WA9_10
  46. WA9_11
Make Master Mix 1
1x          51x
Water        6 ul      306ul
dNTPs      .5ul       25.5ul
R primer    2.5 ul   127.5
F primer    2.5ul     127.5

Master Mix 2
1x            51x
Water          9.17ul       467.67
10x buffer   3.2 ul        163.2
Taq             .13             6.63

Add 1 ul DNA to eac tube
 
Run out gels
  • CA2_1,3,4,5,7 (6/2)
    • redo all
  • CA3_1,2,3,4,5,6
    • 5:3
      6:3
    • redo CA3_1,2,3,4
  • WA12D_1,2,3,4,5,6
    • 1: 0
      2: 3
      3: 4
      4: 3
      5: 5
      6: 0
    • redo WA12D_1,6
  • WA10T_1,2,4,5,6,7
    • 1: 5
      2:4.5
      4: 5
      5: 0
      6: 5
      7: 0
    • redo WA10T_4,6
  • CA6_1,3,7,8,9 redos
    • 1: 2 (low, deg)
      3: 3(low)
      7: 3(low,deg)
      8: 5
      9: 0
  • WA9_2,3,7 redos
    • 2: 2.5(low)
      3: 0
      7: 1
  • OR1_1,2 redos
    • 1: 5
      2: 3.5(low)
  • CA7_6 redos
    • 6: 3.5(low)
  • BC3_12,13,15,16,17,18
    • 12: 5
      13: 4(bright, some smear)
      15: 2(low)
      16: 5
      17: 3.5(smear)
      18: 3.5(very very bright, smear)
  • Note: Re-extract CA6_1,9, WA9_3,7, BC4

Deciding WTF To Do (Nov. 2014)

This was a stressful time in my graduate school career. I felt torn by indecision about what the best wet lab method was to get the data I wanted- given the fact I had very little research funds. When I wrote a few grant proposals in Spring 2014, I had chosen to do the ezRAD method with pooling of 20 individuals from a site per library with one unique barcode per library. As the name suggests, this method is technically straightforward as it uses standard Illumina TruSeq preparation kits, with additional benefits of eliminating PCR-induced bias and not requiring sonication. I had enough money for one kit of 24 barcodes and 1 lane of Illumina HiSeq sequencing. Perfect! But in October, I met with a couple faculty members (one doing plant phylogeograhy using high throughput sequencing, the other a bioinformatics/population genomics guy) and they strongly discouraged against the pooling idea. While there was some support in the literature (Molecular Ecology 2013 Gautier) for the ability to get accurate allele frequencies from pooled data, numerous other papers (such as Molecular Ecology 2014 Anderson) cast doubt. By pooling individuals, I would limit the information I could glean from my sequencing data (ie observed heterozygosity or any form of haplotype analysis) and also be making an a priori assumption that the sites I collected were indeed separate populations.

Alright, so pooling was out. But if I could barely afford 24 barcodes, how could I possibly afford enough unique barcodes in order to sequence 96 individuals on a lane?? Fortunately, a curator at the Field Museum offered to share her lab’s Genotype-by-Sequencing (Elshire-2011-A Robust, Simple Gen) adaptors and barcodes for free. The only problem was they only had 48 barcodes, and my goal was to sequence at least 96 individuals per lane (each lane costs $1100-$1800). This act of scientific kindness led me down another path of obsessive pros and cons lists. Numerous grad students, postdocs, and professors (some I had never met and only stalked on the internet) kindly put up with my frantic emails as I tried to figure out wtf to do. Long story short, I decided to accept the 48 GBS barcodes and use a combinatorial index approach as in Double Digest RADSeq. Excessive pros/cons lists attached.

Cost of Pooling with Different Methods

Method:
  1. Make 40 libraries, with 2 for each population each containing 20 individuals. Sequence on 1 lane. Then resequence individuals from a subset (ie 8 pops) on another lane, will get more loci and better coverage. Can be pops that were not sequenced well previously or pops of interest.
    1. Use ApeKI and GBS
    2. Use ezRAD and REs of choice
    3. ddRAD
  2. Make 40 libraries, with 2 for each population each containing 20 individuals. Sequence on 1 lane. Resequence 8 pools that were poorly sequenced or of greater interest. OR sequence 20 each lane.
    1. Use ApeKI and GBS.
    2. Use ezRAD and REs of choice
    3. ddRAD
GBS (vs ezRAD)
Pros
Cons
$2000 cheaper
uneven sequencing of loci: may need to throw out loci. 2nd run def required
Advice on protocol
Not sensitive to methylation
blocked by some CpG methylation
PCR bias
~65,000 fragments
PCR cleanup vs Ampure beads
1a)40, then 96 GBS
Item
Estimated Cost
Sequencing
2200 (if joined with another group, otherwise 3600)
Adapters
~$200 for Y
RE
~$168
Ligase
80-200
PCR purification kit
$220-$524
Bionalyzer
$1088
Total
$3956-$5780
1b)40, 96 ezRAD
Item
Estimated Cost
Sequencing
3600
Adapters
2880
RE
236
PCR purification kit
0
Bionalyzer
$1088
Total
$7104

1c) and d) 40(48) to 40+96 ddRAD

Item
Estimated Cost
Sequencing
3600
Adapters
$4350
RE
$300
Ligase
80-$201
Ampure beads
$945
Pippin Prep
40-120
Bionalyzer
320-$1088
Total
9636-10,604
Not Pooling:
 
Method:
  1. Use GBS with ApeKI.
    1. 96 libraries of individuals using GBS with ApeKI on one lane. 5 from each of 19 populations or 6 from 16 pops. Look at sequencing, then do another lane with 96 individuals.
    2. 48 on two lanes (so as not to fiddle with adaptors).  ~7 from 13 populations.
  2. 96 libraries of individuals on one lane using ezRAD. 5 from 19 populations or 6 from 16.
    1. Adjust sequencing of additional individuals/pops
  3. 96 on one lane using ddRAD. Resequence additional on 2nd lane.
1a) 96 then 96 GBS
Item
Estimated Cost
Sequencing
1800-3600
Adapters
$200
RE
0-150
Ligase
80-$201
PCR Cleanup
330-524
Bionalyzer
$768-$1536
Total
$3178($33/96)-6211($32/192)
1b) 96 then 96 GBS (real)
Item
Estimated Cost
Sequencing
3600
Primers
$200
RE
$256
Ligase
$256
PCR Cleanup
$279-$389
Bionalyzer
$16
NEB Taq 2X Master Mix
$56
Total
$4663-4773 ($24.5/192)
2)96 then 96 ezRAD
Item
Estimated Cost
Sequencing
1800-3600
Adapters
$2880
RE
$150-300
Bionalyzer
$768-$1536 (12($96)-24($192)
qPCR
72-144
Total
5000($52/96)-7116($37/192)
3) 96 then 96 ddRAD
Item
Estimated Cost
Sequencing
1800-3600
Adapters
$4350
RE
$300
Ligase
80-$201
Ampure beads
$945
Pippin Prep
80-160
Bionalyzer
$768-$1536
Total
8323-11,092($86/96-$57/192)

Oysters on the Brain

This summer I’ve embarked on my first official “field season”, collecting oyster tissue from two species on the West Coast of North America for an exploratory project to help me determine which species will be favorable for my thesis research. If you’re feeling up to a potential snoozefest, here is a proposal I wrote for the Hinds fund, an award for evolutionary biology students at UChicago primarily in their 1st or 2nd years to help support preliminary data collection and develop grant writing skills. It outlines some of the where/what/why/how of this project. Hinds Proposal 2014

Meet The Players

THE NATIVE

Olympia oyster
Olympia oyster (Ostrea lurida)

THE INTRODUCED

Pacific oyster
Pacific or Japanese oyster (Crassostrea gigas)

Project Aims

Starting out, I had three (overly ambitious) aims for this project:

  1. Analyze the fine scale phylogeographic structure of Olympia oysters from San Diego, California to Ladysmith Harbor, British Columbia; incorporate records of local extinctions/introductions into the analysis, estimate parameters of gene flow and dispersal, and correlate with environmental parameters.
  2. Use larvae and adults from both naturalized (producing successful offspring without human aid) and commercially reared populations of Pacific oysters to help elucidate how commercial practices either homogenize or diversify the species (or if there even are any detectable patterns)
  3. Sample newly settled oyster recruits, juveniles, and breeding adults from both species in Washington that experience different means and variances in pH and upwelling. These will be sequenced to identify the areas of the genome that experience allele frequency shifts between life stages.

As of writing, I’m about halfway through my field season and already there have been some reality checks about what is and isn’t feasible, though I’ve picked up some new ideas, too! Stay tuned, as I’ll try to retroactively write up some of my coastal adventures so that I can update the second half of my season in real time.