Category Archives: Project Updates

Now out in AEM: Global-scale structure of the eelgrass microbiome

Ashkaan’s paper was accepted in AEM!

https://www.ncbi.nlm.nih.gov/pubmed/28411219

ABSTRACT

Plant-associated microorganisms are essential for their hosts’ survival and performance. Yet, most plant microbiome studies to date have focused on terrestrial species sampled across relatively small spatial scales. Here we report results of a global-scale analysis of microbial communities associated with leaf and root surfaces of the marine eelgrass Zostera marina throughout its range in the Northern Hemisphere. By contrasting host microbiomes with those of surrounding seawater and sediment, we uncovered the structure, composition and variability of microbial communities associated with eelgrass. We also investigated hypotheses about the assembly of the eelgrass microbiome using a metabolic modeling approach. Our results reveal leaf communities displaying high variability and spatial turnover, that mirror their adjacent coastal seawater microbiomes. In contrast, roots showed relatively low compositional turnover and were distinct from surrounding sediment communities — a result driven by the enrichment of predicted sulfur-oxidizing bacterial taxa on root surfaces. Predictions from metabolic modeling of enriched taxa were consistent with a habitat filtering community assembly mechanism whereby similarity in resource use drives taxonomic co-occurrence patterns on belowground, but not aboveground, host tissues. Our work provides evidence for a core eelgrass root microbiome with putative functional roles and highlights potentially disparate processes influencing microbial community assembly on different plant compartments.

IMPORTANCE Plants depend critically on their associated microbiome, yet the structure of microbial communities found on marine plants remains poorly understood in comparison to terrestrial species. Seagrasses are the only flowering plants that live entirely in marine environments. The return of terrestrial seagrass ancestors to oceans is among the most extreme habitat shifts documented in plants, making them an ideal test bed for the study of microbial symbioses with plants that experience relatively harsh abiotic conditions. In this study, we report results of a global sampling effort to extensively characterize the structure of microbial communities associated with the widespread seagrass species, Zostera marina or eelgrass, across its geographic range. Our results reveal major differences in the structure and composition of above- versus belowground microbial communities on eelgrass surfaces, as well as their relationships with the environment and host.

Preprint Available: Global-scale structure of the eelgrass microbiome

Abstract

Plant-associated microorganisms are essential for their hosts’ survival and performance. Yet, most plant microbiome studies to date have focused on terrestrial species sampled across relatively small spatial scales. Here we report results of a global-scale analysis of microbial communities associated with leaf and root surfaces of the marine eelgrass Zostera marina throughout its range in the Northern Hemisphere. By contrasting host microbiomes with those of their surrounding seawater and sediment communities, we uncovered the structure, composition and variability of microbial communities associated with Z. marina. We also investigated hypotheses about the mechanisms driving assembly of the eelgrass microbiome using a whole-genomic metabolic modeling approach. Our results reveal aboveground leaf communities displaying high variability and spatial turnover, that strongly mirror their adjacent coastal seawater microbiomes. In contrast, roots showed relatively low spatial turnover and were compositionally distinct from surrounding sediment communities – a result driven by the enrichment of predicted sulfur-oxidizing bacterial taxa on root surfaces. Metabolic modeling of enriched taxa was consistent with an assembly process whereby similarity in resource use drives taxonomic co-occurrence patterns on belowground, but not aboveground, host tissues. Our work provides evidence for a core Z. marina root microbiome with putative functional roles and highlights potentially disparate processes influencing microbiome assembly on different plant compartments.

 

http://biorxiv.org/content/early/2016/11/28/089797

Culturing Bacterial Isolates from the Seagrass Microbiome

My name is Karley Lujan and I am an undergraduate working on culturing bacterial isolates from the Seagrass microbiome. I joined this project because I am interested in learning about what information we can obtain from studying microbiomes. I think it is fascinating that although we can’t see microorganisms they are extremely prevalent and can have crucial roles in biological systems. The focus at the beginning of this project was to take Seagrass samples from Bodega Bay, create culture samples, and use Sanger sequencing of the 16S rRNA to identify what we grew. Seagrass and sediment samples were taken from Bodega Bay, CA. Then, in order to obtain isolates from the seagrass, we focused on the leaves, roots, and sediment. What we were able to successfully extract DNA from were identified as Shewanella, Pseudoalteromonas, Colwellia, Tenacibaculum, Vibrio and Alteromonas.

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Me (Karley Lujan) at the 2016 UC Davis Undergraduate Research Conference.
CVK_y3zVEAA229b.jpg large (1)
Agarolytic Bacteria on Marine Broth. Tentative identification: Shewanella

Methods:

  • Sample preparation: Dilutions of sediment with PBS, PBS rinse of roots and leaves, ground and crushed leaves with PBS
  • Culturing: Plated the PBS sample solutions onto two of each of the following plate types; one plate for 25℃ and the other at 4℃

Agar Plates/Liquid Media

      • Nitrogen Free
      • Seawater Agar
      • Seawater Medium
      • Difco Marine Broth
  • Selected Colonies: After there was significant growth on the plates we selected various interesting colonies and isolated them by dilution streaking. Single colonies were then grown overnight in the appropriate liquid media and at the appropriate temperature
  • DNA Extraction: Genomic DNA extractions were performed and glycerol stocks were made using the successful liquid cultures. Extracted DNA then went through 16S rRNA gene PCR and gel electrophoresis in order to confirm that enough DNA was present for Sanger Sequencing
  • Sanger Sequencing: 16S rRNA sequences for each isolate were ran through BLAST and phylogenetic trees were built in order to obtain tentative identifications for the isolates

Results: After Sanger sequencing, the data was ran through BLAST to obtain a tentative identification and determine whether or not the microbe was a good candidate for sequencing.

Shewanella: Electrogenic- An electron generator that can be used in microbial fuel cells.

Colwellia: Cold-adapted

Vibrio: Some species of Vibrio can go through morphogenetic changes after going from a liquid to a solid surface. This leads them to change from swimmer cells to swarmer cells.

Pseudomonas: Two bacterial isolates were cable of growing on Nitrogen-Free agar plates at 25⁰C. Identified as part of the genera Pseudomonas, there are some species of this genera capable of aerobically fixing nitrogen. These are of particular interest as we will be further investigating which nitrogen-fixing bacteria are essential for seagrass health.

Currently I am beginning to look at the genomes of the bacteria we decided to sequence and I am also working with bacteria that are capable of growing on the nitrogen-free agar plates. At first it was difficult to extract the DNA from these bacteria but now both have been tentatively identified as Pseudomonas through sanger sequencing of the 16S gene. This is interesting because there aren’t many Pseudomonas that can fix nitrogen which is what these two must be doing in order to survive on the nitrogen-free plates. These two bacteria also have different morphologies which means they could be different species in the genus Pseudomonas. Due to their morphological similarities yet ability to grow on nitrogen-free agar, I think these two bacteria are very interesting and we will be finding out more about them by sequencing and analyzing their genomes.

 

Adventures in SEM

SEM demo room @ UCDavis. Hitachi TableTop is the large rectangular machine on the left.
SEM demo room @ UCDavis. Hitachi TableTop is the large rectangular machine on the left.

Some of us at team Seagrass participated in a workshop on SEM (scanning electron microscopy) sponsored by the Electron Microscopy Lab at UC Davis. For this week only Hitachi Tabletop SEM was made available to researchers to test out. SEM is a great tool to produce high resolution images, especially for samples for which preparation techniques would otherwise alter the sample. We decided this would be a great opportunity to visually explore microbial diversity on seagrass roots/leaves/rhizomes. The amount of sample and preparation is minimal relative to other techniques. We’ve been particularly concerned with our previous FISH images, as FISH requires many washes, and may be removing microbes from the surface of our samples.

Samples on mount ready for SEM
Samples on mount ready for SEM

We put a minimal amount of sample of root (top), fresh leaf (right), decaying leaf (bottom), and rhizome cross section (left) on a piece of carbon conductive paper (sticky on both sides), which was stuck on top of the SEM sample mount. We then inserted the mount into the SEM and turned on the vacuum. The vacuum for this particular SEM is actually a partial vacuum, which maintains a small amount of air molecules within the chamber, which produces a better image in the absence of coating your sample with conductors.

The results were great! We were able to see a lot of diatoms spread out across the leaf surface, as well as some interesting plaque (?) formations on the root tips. We also noticed differences in diatom abundance between the live and decaying leaves, with the live leaf being completely covered with diatoms. Diatoms are marine microbial eukaryotes (Heterokonts) that form silica based outer layers, often resembling complex geometric patterns. Diatom assemblages have previously been characterized in Thalassia testudinum and in Zostera marina.  We’re excited to use the SEM to explore microbial diversity on additional seagrass species and freshwater relatives within the Alismatales. Hopefully these results can help inform our future culturing experiments.

Root tip with strange filaments
Root tip with strange filaments
Diatoms on rhizome surface
Diatoms on rhizome surface
Close-up of diatoms on live leaf
Close-up of diatoms on live leaf
Diatom distribution on live leaf
Diatom distribution on live leaf
Diatom distribution on decaying leaf
Diatom distribution on decaying leaf

CA sampling for project phylogeny

A few of us at Team Seagrass have been doing fieldwork in Northern CA in order to collect seagrass relatives. We have specifically been targeting freshwater species (highlighted in blue in the below phylogeny). We will continue with marine and brackish species when the tides in the San Francisco Bay are lower.

From https://phylogenomics.wordpress.com/people/post-docs/jenna-lang/seagrass/photo-4/: This is a phylogeny of the seagrasses and their aquatic relatives. This tree was built using parsimony and ~1200bp alignment of the rbcL gene. From Les and Cleland, 1997.
From https://phylogenomics.wordpress.com/people/post-docs/jenna-lang/seagrass/photo-4/: This is a phylogeny of the Alismatales, including the seagrasses and their aquatic relatives. This tree was built using parsimony and ~1200bp alignment of the rbcL gene. From Les and Cleland, 1997.

We have been following the methods detailed in https://seagrassmicrobiome.org/sample-collection-and-preservation/ and have managed to collect a few representative species from the following clades: PotamogetonaceaeAlismataceae, Hydrocharitaceae, Najadaceae, and the Lilaceae.

crap.001
Example leaf and root from Alisma species, collected at Cosumnes River

For further plant identification and the construction of a ‘host phylogeny’ we will be using chloroplast DNA markers.

In addition to what we have sampled thus far, we have also managed to sample some outgroups (MyriophyllumCeratophyllum, and Camboba). We will hopefully be able to sample additional outgroup species shown on the above tree.

We have been successful in 7 out of the 10 locations that we have tried. The lack of success so far can be attributed to drought conditions in CA and the increase in invasive submerged aquatic vegetation (e.g. Myriophyllum, Water Milfoil; and Eichhornia crassipes, Water Hyacinth). Water Milfoil and Water Hyacinth form dense mats below and above water, respectively, outcompeting native vegetation. Even in sites where we been successful, much of the area we surveyed has been overrun with these two invasive species.

Obviously drought has also been an issue for us, as many of the lakes and tributaries have little to no water in them this year, resulting in a lack of any aquatic vegetation. Water levels in Folsom Lake, for example, are so far reduced that the Park Service has had to build additional parking lots on the water side of the boat ramp in order for people to be able to get close to the new shoreline.

Map of Folsom Lake, with parking instructions drawn on by park official. X marks location of new parking lot, which is still at 15 minute walk to the shoreline.
Map of Folsom Lake, with parking instructions drawn on by park official. X marks location of new parking lot, which is still at 15 minute walk to the shoreline.

In the following weeks we will head up to the foothills in search of remaining freshwater species, as well as explore the Bay Area coastline and salt marsh/Delta area for marine and brackish species. Stay tuned!

Potomac River Samples – Finally, DNA

At last, all the Potomac River Seagrass samples have had their DNA extracted via MoBio Powersoil kits (reagents, tubes, and pipettes, oh my!). All the samples showed promising amounts of DNA going into PCR. Unfortunately, after doing the first 12 samples and finding only 4 that amplified, I’m a little discouraged. I still have the rest of the ~60 samples to go through, so hopefully there will be enough bacterial DNA to amplify in the rest of the samples to do some meaningful analyses. I suspect most of the DNA extracted was non-bacterial though, so it might be hard getting sequences from these plants.

Introducing Biogeography 2

It’s bigger, it’s better, it’s Biogeography 2!

About a year ago I started an Intra-plant biogeography project. Limited in scope, this project’s primary aim was to determine how much variation there was in the microbial communities across a single plant in “high resolution.” The goal was to determine whether it mattered where our ZEN collaborators cut their samples from along the roots and leaves.

The general project was this: Cut a plant into about 50 strategically chosen pieces and look at the community variation across the surface.

We got some really interesting results which I presented in a poster at the 2014 Lake Arrowhead Microbial Genomics Conference.

One thing that always bothered me about these results were that they were for only one plant. I didn’t know if the cool patterns I was seeing were normal or a fluke. That’s where Biogeography 2 comes in, it’s a continuation of the first project but with more replicates (five, to be precise) all collected at the same time and from the same place. In the coming weeks I’ll be processing these samples and updating you about the progress.

This week’s update:

This week I finally was able to mutilate  dissect the plants and now we can begin extracting DNA from the samples. Here are some pictures of plants prior to dissection.

DSC_0082

For a plant that withstands daily tidal forces, seagrass are surprisingly delicate when taken out of water. When they dry out, they crumble so I try to section them as fast as possible to prevent drying.

DSC_0073

Sample preparation includes painstakingly disentangling these roots from each other and from the shoots without breaking them. (About a 2 hour process per plant).

and so it begins… running the ZEN data through my IPython notebook

1. Downloaded the notebook from here:

http://jennomics.github.io/QIIMEbyJennomics/

2. immediate failure:

!validate_mapping_file.py -m $mapping_file
Traceback (most recent call last):
  File "/macqiime/QIIME/bin/validate_mapping_file.py", line 14, in <module>
    from qiime.util import parse_command_line_parameters, get_options_lookup,\
  File "/macqiime/lib/python2.7/site-packages/qiime/util.py", line 26, in <module>
    import gzip
  File "/Users/Jenna/anaconda/lib/python2.7/gzip.py", line 10, in <module>
    import io
  File "/Users/Jenna/anaconda/lib/python2.7/io.py", line 51, in <module>
    import _io
ImportError: dlopen(/Users/Jenna/anaconda/lib/python2.7/lib-dynload/_io.so, 2): Symbol not found: __PyInt_AsInt
  Referenced from: /Users/Jenna/anaconda/lib/python2.7/lib-dynload/_io.so
  Expected in: dynamic lookup

2. because my computer is so shiny and new, I don’t have any microsoft applications installed (yet?). I’m using “Numbers,” which I’ve never used before and does not have tab-delimited format as an export option. I’m hoping that the problem was that I was trying to use a .csv file for my mapping file. So, I converted it:

perl -pi -e ‘s/\,/\t/g’ ZEN.csv

and tried again. Nope!

3. tried googling this:

ImportError: dlopen Symbol not found

cannot understand what I see there, but it does convince me that it’s probably an anaconda problem

tried googling this:

Expected in: dynamic lookup anaconda

I felt pretty hopeful when the first hit was this:

issues with starting ipython notebook – Google Groups

but, that was an unresolved issue. I did notice that the issue might actually be with lib-dynload, whatever the hell that is.

So, googled this:

anaconda lib-dynload

and ended up here:

http://stackoverflow.com/questions/19124436/linking-problems-with-anaconda-when-using-ld-library-path

something in there made it click that the path to python should be via macqiime, and you can see in the traceback that it starts off with the macqiime python, but then switches to the anaconda python. I always feel better about asking the community a question if I have some sense of what might be going on. So, I’ll post it on the QIIME forum now.

While I was writing the post, I thought to try to run the command from the command line instead of the notebook, and it worked, so that really helps narrow things down…

ALSO, while posting it, I realized that I haven’t update to QIIME 1.9 yet. Blach.

installing IPython notebook

Yesterday, I installed macqiime, and today, I have just a few minutes to install IPython notebook.

Installation instructions are here:

http://ipython.org/install.html

pip install "ipython[notebook]"

This didn’t work because I don’t have pip. Instructions for installing pip:

https://pip.pypa.io/en/latest/installing.html

It says that pip should be included with Python 2.7.9 and later, and (according to the handy qiime config output from yesterday) I know that I have 2.7.3.

It seems dumb to just install pip, because I’ll probably need to install lots of other things as well. I should probably upgrade to a later version of python, but in my experience, upgrading python breaks all sorts of things. However, I have nothing to lose, since this is a brand new machine, so I installed the Anaconda distribution of python.

http://continuum.io/downloads

…and now pip works and I have IPython notebook!

installing macqiime

Within the span of 1 week, I set up my new super-powerful Mac Pro, we got all of the ZEN sequence data back, and QIIME version 1.9 is live! I also posted my IPython notebook for a basic QIIME analysis.http://jennomics.github.io/QIIMEbyJennomics/

Quite a confluence of events…

Anyway… I’m christening my new machine with QIIME.

Notes on macqiime install:

1. I went through the installation instructions, including the optional add-ons with no glitches here:

http://www.wernerlab.org/software/macqiime/macqiime-installation#install

2. I ignored AmpliconNoise because I do not use 454 data.

3. I could not get Topiary Explorer to work. At first, there was a problem with the security, but I figured out how to add exceptions, but then it still didn’t work, and the error message said: “Unable to launch application.” Then, I clicked on the Details button, and I think this describes the problem:

Caused by: java.net.URISyntaxException: Relative path in absolute URI: file://topiaryexplorer1.0.jar

But, I’m not sure how to fix it, so I decided to move on and come back to Topiary Explorer when/if I need it.

4. In the bit about installing R, I noticed this:

Please note that even if you installed R and these libraries previously for MacQIIME 1.8.0, you should still upgrade to/install the latest version of R, 3.1.2, and re-install all these R packages to get everything working.

And, that’s how I learned that QIIME 1.9 was out there. BUT, it doesn’t look like macqiime has been updated, so it installed QIIME 1.8 instead. Maybe that’s because it’s a “release candidate” at this point? Anyway, I’ll have to go back and update QIIME somehow. Macqiime appears to be working. See below for the output of print_qiime_config.py -t

System information
==================
Platform:    darwin
Python version:    2.7.3 (default, Dec 19 2012, 09:12:08)  [GCC 4.2.1 (Apple Inc. build 5666) (dot 3)]
Python executable:    /macqiime/bin/python

Dependency versions
===================
PyCogent version:    1.5.3
NumPy version:    1.7.1
matplotlib version:    1.1.0
biom-format version:    1.3.1
qcli version:    0.1.0
QIIME library version:    1.8.0
QIIME script version:    1.8.0
PyNAST version (if installed):    1.2.2
Emperor version:    0.9.3
RDP Classifier version (if installed):    rdp_classifier-2.2.jar
Java version (if installed):    Not installed.

QIIME config values
===================
blastmat_dir:    None
sc_queue:    all.q
topiaryexplorer_project_dir:    None
pynast_template_alignment_fp:    /macqiime/greengenes/core_set_aligned.fasta.imputed
cluster_jobs_fp:    /macqiime/QIIME/bin/start_parallel_jobs.py
pynast_template_alignment_blastdb:    None
assign_taxonomy_reference_seqs_fp:    /macqiime/greengenes/gg_13_8_otus/rep_set/97_otus.fasta
torque_queue:    friendlyq
template_alignment_lanemask_fp:    /macqiime/greengenes/lanemask_in_1s_and_0s
jobs_to_start:    1
cloud_environment:    False
qiime_scripts_dir:    /macqiime/QIIME/bin/
denoiser_min_per_core:    50
working_dir:    /tmp/
python_exe_fp:    /macqiime/bin/python
temp_dir:    /tmp/
blastall_fp:    blastall
seconds_to_sleep:    60
assign_taxonomy_id_to_taxonomy_fp:    /macqiime/greengenes/gg_13_8_otus/taxonomy/97_otu_taxonomy.txt
….F…………………………
======================================================================
FAIL: test_ampliconnoise_install (__main__.QIIMEDependencyFull)
AmpliconNoise install looks sane.
———————————————————————-
Traceback (most recent call last):
File “/macqiime/QIIME/bin/print_qiime_config.py”, line 392, in test_ampliconnoise_install
“$PYRO_LOOKUP_FILE variable is not set. See %s for help.” % url)
AssertionError: $PYRO_LOOKUP_FILE variable is not set. See http://qiime.org/install/install.html#ampliconnoise-install-notes for help.

———————————————————————-
Ran 35 tests in 0.456s

FAILED (failures=1)