All posts by Cassie Ettinger

Marine Fungi Workshop

I just returned from a marine fungi workshop set up by Amy Gladfelter and supported by the Gordon and Betty Moore Foundation. The workshop was from May 7-9th at the Marine Biological Laboratory in Woods Hole, MA. This was actually my second trip to Woods Hole, my first was in summer of 2015 to attend the Microbial Diversity course (click here to read a cheesey poem I wrote about the course).

The workshop started with everyone giving 5 minute lightning talks about their research. It was my first time presenting my research ideas to people outside of UC Davis and even though it was only a 5 minute presentation, I was scared to death. I am pretty sure I was literally shaking in the moments leading up to my talk and my imposter syndrome was yelling at me to run far far away so that the real mycologists (doubly scary since they were mostly all professors) wouldn’t know they’d invited a eco-evolutionary microbiologist / bioinformagician into their midst. I can’t really remember anything that happened in those 5 minutes, but I walked away feeling like I had crushed it (take that imposter syndrome).

After the talks, we discussed what we thought were some big issues in marine mycology as a group before breaking up into 4 smaller groups with the goal of drafting white papers on these issues.

The 4 smaller group topics were:

  1. Who is out there? Identification and isolation of fungi from different parts of the marine environment
  2. How can marine fungi be studied? Establishing model systems to discover new biology
  3. What are fungi doing to influence the geochemical cycle of the ocean? Establishing the function of fungi in chemical cycling and contributions to climate
  4. How are fungi interacting with and shaping the marine biosphere? Identification of fungal interactions across scales of life in the ocean
Some of the dominant themes that resulted from these conversations were (1) a desire to  inform both scientists and non-scientists of the presence of fungi in the ocean; (2) to impart and quantify the importance of the roles of marine fungi in the ocean; (3) the unclear definition of marine fungi and whether or not this definition includes facultative marine fungi, transient terrestrial fungi or freshwater / brackish fungi; (4) our current lack of understanding of the genetic, phylogenetic, functional and ecological diversity of marine fungi and the spatial scales at which they exist in the marine environment; (5) the lack of standardized protocols for the study of fungi more generally and a need for improved / expanded databases for fungal sequence data that potentially incorporate phylogeny.

I got to meet a bunch of awesome people from a variety of fields (including systematics, cell biology, genetics, chemistry, bioinformatics, etc), some of whom I had heard a lot about / seen before on twitter and others who were completely new to me! I only wish it had been 1-2 days longer to further promote networking opportunities and collaborative discussions. Despite the jam-packed workshop schedule, we somehow managed to fit in a boat trip on one of the MBL’s collection vessels, the Gemma.

Throughout the conference, I realized a few things (1) I should probably be going to and giving talks at more conferences; (2) networking skills are extremely important; (3) I need to learn more about fungal taxonomy and systematics; (4) I am now super excited to look at and incorporate fungi in some of my other non-seagrass projects; (5) working on my computer on a bus is not a good idea and makes me extremely motion sick.
20180507_154134
Found some microbes in Woods Hole, but no marine fungi 😦
This workshop served as a breathe of fresh air for me and helped renew my excitement for analyzing my seagrass-associated fungal ITS data. It also gave me a few cool ideas of things to do moving forward. I am extremely grateful that I had the opportunity to attend and that the Moore foundation was able to bring us all together. I can’t wait for the next marine fungi meet-up!

Now out in PeerJ: Microbiome succession during ammonification in eelgrass bed sediments

https://peerj.com/articles/3674/?td=bl

Abstract

Background

Eelgrass (Zostera marina) is a marine angiosperm and foundation species that plays an important ecological role in primary production, food web support, and elemental cycling in coastal ecosystems. As with other plants, the microbial communities living in, on, and near eelgrass are thought to be intimately connected to the ecology and biology of eelgrass. Here we characterized the microbial communities in eelgrass sediments throughout an experiment to quantify the rate of ammonification, the first step in early remineralization of organic matter, also known as diagenesis, from plots at a field site in Bodega Bay, CA.

Methods

Sediment was collected from 72 plots from a 15 month long field experiment in which eelgrass genotypic richness and relatedness were manipulated. In the laboratory, we placed sediment samples (n = 4 per plot) under a N2 atmosphere, incubated them at in situ temperatures (15 °C) and sampled them initially and after 4, 7, 13, and 19 days to determine the ammonification rate. Comparative microbiome analysis using high throughput sequencing of 16S rRNA genes was performed on sediment samples taken initially and at seven, 13 and 19 days to characterize changes in the relative abundances of microbial taxa throughout ammonification.

Results

Within-sample diversity of the sediment microbial communities across all plots decreased after the initial timepoint using both richness based (observed number of OTUs, Chao1) and richness and evenness based diversity metrics (Shannon, Inverse Simpson). Additionally, microbial community composition changed across the different timepoints. Many of the observed changes in relative abundance of taxonomic groups between timepoints appeared driven by sulfur cycling with observed decreases in predicted sulfur reducers (Desulfobacterales) and corresponding increases in predicted sulfide oxidizers (Thiotrichales). None of these changes in composition or richness were associated with variation in ammonification rates.

Discussion

Our results showed that the microbiome of sediment from different plots followed similar successional patterns, which we infer to be due to changes related to sulfur metabolism. These large changes likely overwhelmed any potential changes in sediment microbiome related to ammonification rate. We found no relationship between eelgrass presence or genetic composition and the microbiome. This was likely due to our sampling of bulk sediments to measure ammonification rates rather than sampling microbes in sediment directly in contact with the plants and suggests that eelgrass influence on the sediment microbiome may be limited in spatial extent. More in-depth functional studies associated with eelgrass microbiome will be required in order to fully understand the implications of these microbial communities in broader host-plant and ecosystem functions (e.g., elemental cycling and eelgrass-microbe interactions).

Preprint available: Microbiome succession during ammonification in eelgrass bed sediments

https://peerj.com/preprints/2956

Abstract

Background. Eelgrass (Zostera marina) is a marine angiosperm and foundation species that plays an important ecological role in primary production, food web support, and elemental cycling in coastal ecosystems. As with other plants, the microbial communities living in, on, and near eelgrass are thought to be intimately connected to the ecology and biology of eelgrass. Here we characterized the microbial communities in eelgrass sediments throughout an experiment to quantify the rate of ammonification, the first step in early remineralization of organic matter, or diagenesis, from plots at a field site in Bodega Bay, CA.

Methods. Sediment was collected from 72 plots from a 15 month long field experiment in which eelgrass genotypic richness and relatedness were manipulated. In the laboratory, we placed sediment samples (n= 4 per plot) under a N2 atmosphere, incubated them at in situ temperatures (15 oC) and sampled them initially and after 4, 7, 13, and 19 days to determine the ammonification rate. Comparative microbiome analysis using high throughput sequencing of 16S rRNA genes was performed on sediment samples taken initially and at 7, 13 and 19 days to characterize the relative abundances of microbial taxa and how they changed throughout early diagenesis.

Results. Within-sample diversity of the sediment microbial communities across all plots decreased after the initial timepoint using both richness based (observed number of OTUs, Chao1) and richness and evenness based diversity metrics (Shannon, Inverse Simpson). Additionally, microbial community composition changed across the different timepoints. Many of the observed changes in relative abundance of taxonomic groups between timepoints appeared driven by sulfur cycling with observed decreases in sulfur reducers (Desulfobacterales) and corresponding increases in sulfide oxidizers (Alteromonadales and Thiotrichales). None of these changes in composition or richness were associated with ammonification rates.

Discussion. Overall, our results showed that the microbiome of sediment from different plots followed similar successional patterns, which we surmise to be due to changes related to sulfur metabolism. These large changes likely overwhelmed any potential changes in sediment microbiome related to ammonification rate. We found no relationship between eelgrass presence or genetic composition and the microbiome. This was likely due to our sampling of bulk sediments to measure ammonification rates rather than sampling microbes in sediment directly in contact with the plants and suggests that eelgrass influence on the sediment microbiome may be limited in spatial extent. More in-depth functional studies associated with eelgrass microbiome will be required in order to fully understand the implications of these microbial communities in broader host-plant and ecosystem functions (e.g. elemental cycling and eelgrass-microbe interactions).

Now out in PeerJ: Microbial communities in sediment from Zostera marina patches, but not the Z. marina leaf or root microbiomes, vary in relation to distance from patch edge

https://peerj.com/articles/3246/?td=bl

tl;dr – The microbes (bacteria) on plant parts  (root, leaf) and near-by sediment were different from each other. We did not find a difference between the microbes on  eelgrass leaves or roots at the edge of a patch versus the middle of the patch. However, the microbes in sediments from different locations in the patch (middle, edge, outside of the patch) differed and these differences correlated with eelgrass density.

Abstract

Background

Zostera marina (also known as eelgrass) is a foundation species in coastal and marine ecosystems worldwide and is a model for studies of seagrasses (a paraphyletic group in the order Alismatales) that include all the known fully submerged marine angiosperms. In recent years, there has been a growing appreciation of the potential importance of the microbial communities (i.e., microbiomes) associated with various plant species. Here we report a study of variation in Z. marina microbiomes from a field site in Bodega Bay, CA.

Methods

We characterized and then compared the microbial communities of root, leaf and sediment samples (using 16S ribosomal RNA gene PCR and sequencing) and associated environmental parameters from the inside, edge and outside of a single subtidal Z. marina patch. Multiple comparative approaches were used to examine associations between microbiome features (e.g., diversity, taxonomic composition) and environmental parameters and to compare sample types and sites.

Results

Microbial communities differed significantly between sample types (root, leaf and sediment) and in sediments from different sites (inside, edge, outside). Carbon:Nitrogen ratio and eelgrass density were both significantly correlated to sediment community composition. Enrichment of certain taxonomic groups in each sample type was detected and analyzed in regard to possible functional implications (especially regarding sulfur metabolism).

Discussion

Our results are mostly consistent with prior work on seagrass associated microbiomes with a few differences and additional findings. From a functional point of view, the most significant finding is that many of the taxa that differ significantly between sample types and sites are closely related to ones commonly associated with various aspects of sulfur and nitrogen metabolism. Though not a traditional model organism, we believe that Z. marina can become a model for studies of marine plant-microbiome interactions.

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

MBL Microbial Diversity Summer Course

I spent this past summer at the MBL Microbial Diversity summer course and I’ve written a post about it which can be found here: http://microbe.net/2015/09/28/mbl-microbial-diversity-summer-course/

While at the course, I also cultured a handful of microbes from seagrass leafs/roots and several different amoeba from seagrass bed sediments including one really cool “follow the leader” snail slime like amoeba (slime mold?).

Visualizing Microbiome Data: Choropleth Style

A Pipette and an Open Mind

Recently I’ve needed to visualize spatial changes in my microbiome data that are easily interpretable by other people. The best solution I’ve come across is simply projecting the data onto a drawing of my organism. Like this:

Zostera marina high resolution Alpha diversity of samples from pieces of a seagrass plant projected onto a drawing of that plant.

I’ve used SitePainter to produce these in the past, and in some ways it’s great. I got the figure I wanted and I’ve received a lot of positive feedback about it. The only problem is it has a steep learning curve and is the most dysfunctional GUI I’ve ever come across (generating the figure above took several days of my time, the first time). So when it became necessary to produce over forty more images like it I decided to search for a better way.

My first instinct was that there should be an easy way…

View original post 694 more words

ASM Highlights

The Seagrass Team (Hannah, Jenna & I) hit up ASM this year which was in New Orleans. In case you missed ASM, Jonathan took the effort to compile all the tweets together (#ASM2015) and his efforts can be found: here.

However in case you don’t want to wade though thousands of tweets I’ve included some some brief highlights:

NCBI’s Targeted Loci Blast (MOLE-BLAST)

  • Using MOLE-BLAST you can blast specific 16S or ITS sequences against NCBI currated databases for those marker genes. This seems like it could be really useful if you want to identify bacterial or fungal taxonomy using a marker gene approach. Also, MOLE-BLAST appears to use a tree based approach to help you find the nearest neighbor for taxonomy assignment.

Carl Zimmer’s Talk on Microbiomes and the Hyperbolome

The Session that kept Redefining the Tree of Life (aka Unearthing the Dark Matter of Microbial Metabolism and Diversity)

  • Unfortunately, I missed this session, but apparently both Brett Baker (@archaeal) and Laura Hug (@LAHug_) shook things up
  • Brett Baker brought Thorarchaeota to the party, a monophyletic group that looks like it branches between Lokiarchaeota and Eukaryotes
  • Then Laura Hug showed up with Woesearchaeota and Pacearcheoata, not sure where they fit in, but cool

Contributions to “Extreme” Microbiology by Female Scientists Session

  • The whole session was awesome (and extreme), but Emmie de Wit gave a heartfelt (and tear producing) talk on being a first responder to the Ebola outbreak in Liberia
  • Also, I now really want to go to the Bonneville Salt Flats that Betsy Kleba talked about

Honorable mentions: John Zehr (Talk on open ocean Nitrogen fixing symbionts), David Baltrus (Talk on fungal endophytes with bacterial symbionts in their hyphae), Michael Wagner (Talk on syntrophy between nitrite and ammonia oxiders and alternative substrates for nitrification), Tom Marshburn (A real life astronaut!), Tom Sharpton (A Shotgun Metagenome Annotation Pipeline (ShotMAP))

Hannah and I presented posters at the meeting, her poster (left, #1237) and my poster (right, #1236) can be seen below:

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Of course, Team Seagrass wasn’t the only ones from the Eisen lab presenting at ASM. David Coil (@davidacoil), Srijak Bhatnagar (@srijakbhatnagar) and Megan Krusor (@MKrusor) also presented posters this year! See tweets with photos below.

Some might say that the real highlight of ASM was New Orleans itself; it was my first time there and I really enjoyed being immersed in the culture (the music!), city and food (especially the food). Below is a picture of me touching the Mississippi River (my first time!). Unfortunately, the water was too murky to search for any seagrass or seagrass relatives.

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Fungal ITS Taxonomy Problem: SOLVED (for now)

The past couple weeks (maybe months?) I’ve been struggling with analyzing some fungal ITS data that we have for our Edge Effects side project. No one in our lab really specializes in fungal barcoding (or fungal anything) so we became sheep and followed the mainstream path. We amplified the ITS region, between the small subunit and large subunits of RNA, which was to our knowledge the “chosen one” for fungal barcoding, using ITS1F and ITS2 primers. ITS appears to serves its purpose in terms of detailed classification (family/genus taxonomic levels) but it is definitely not a perfect barcode – for one ITS reads cannot be aligned (perhaps due to too much variation between reads, insertions, deletions, length variation, etc) which makes the reads useless by themselves for phylogenetic approaches.

Before this particular dataset fell into my hands, it was in Jenna’s and when issues with the ITS dataset arose, she turned to twitter for answers (part 1 and part 2). The conclusion – due to our desire for phylogenetic analysis it is highly likely that future fungal analysis will not be done using ITS as ultimately we care more about phylogeny than taxonomy.

That is great – but we still have our ITS dataset, what do we do with it?

I essentially did what they do here in this tutorial which I of course found after figuring out what to do from scratch. I used the UNITE ITS database to cluster my forward unmerged reads into OTUs in QIIME using UCLUST. I also used UCLUST to assign taxonomy (because it was the default option). I then did some basic filtering using filter_taxa_from_otu_table.py and filter_otus_from_otu_table.py to remove singletons, mitochondria, chloroplasts and unassigned (at kingdom level) taxa. This is where things began to go wrong (if they weren’t already wrong to start with).

I summarized my biom table using biom summarize-table and I saw this:

Counts/Sample summary:
Min: 0.0
Max: 838.0
Median: 30.000
Mean: 100.512
Std. dev.: 201.292

What happened to all my sequences?? Better yet, are there even fungi on seagrass? Is what we are seeing the result of low fungal biomass????

Let the investigation begin. I decided to look at what my biom table looked like before I filtered out the unassigned reads. This is what I saw.

Min: 14.0
Max: 48889.0
Median: 2847.000
Mean: 6001.653
Std. dev.: 9287.627

Now, that looks a bit better… except that the “unassigned” reads could be anything (seagrass, jellyfish, bacteria, fungi, sponges, etc). Since we want to do a “fungal” analysis this just won’t do. So to investigate further, I downloaded NCBI’s nucleotide “nt” database. Approx ~4250 OTU’s in my dataset were classified as “Unassigned” so I pulled these out and locally blasted them against the “nt” database to get some idea of their taxonomy. What I found was that my “Unassigned” OTUs were seagrass, jellyfish, bacteria, sponges and lots and lots of uncultured fungi. Of my ~4250 OTU’s, ~3250 hit something in the “nt” database and ~700 of hit something with >70% identity over >70% of the query OTU length.  So there are obviously fungi (or fungi-like sequences) in my dataset that aren’t being identified using the method for taxonomic assignment I’ve been using (UCLUST & UNITE).

On a whim while writing this blog post about the dreary nature of ITS, I took a second look at the earlier mentioned tutorial. On the surface, it looks identical to what I did with my dataset (reassuring), but I then noticed they were using a mysterious parameter file. Perhaps this parameter file was filled with rainbows, pixie dust and unicorns that could solve all my fungal problems? To investigate further, I downloaded and took a peak at this mythical parameter file. Cue dramatic music. Low and behold, they are using the “blast” method for taxonomic assignment over UCLUST. So I thought what the heck, I’ll try anything at this point to make this fungal data usable, let’s give it a go. Of course (because this is how my life seems to be going recently) using the “blast” method of taxonomic assignment worked like magic. My new biom table summary (and this is after removing OTUs with “No blast hit”) looks like this:

Min: 7.0
Max: 47199.0
Median: 2344.000
Mean: 5441.093
Std. dev.: 9146.485

According to the log file, using the “blast” method 4717 sequences were inspected and only 1796 could not be identified. This is a huge improvement from before where ~4250 were “Unassigned”. I will note here, that upon investigating the blast assigned taxonomies, I do see a lot of unidentified fungi so this solution might not work for you if you care about specific taxonomy. I still have to analyze this new biom table which since I can’t use phylogenetic approaches will be its own hurdle, but at least I have enough truly “fungal” data to analyze now. Thinking back on all of my struggles, I am so incredibly angry that one silly QIIME parameter was what was keeping me from moving forward. Even before this I was wary of what the default QIIME script options meant for my data, but moving forward I’ll be even more vigilant in my choice of programs and parameters. This entire situation is equal parts ridiculous, embarrassing, frustrating and dumb luck. Perhaps, the craziest part is that had I not decided to write a blog post about my problems with ITS, I would never have found the solution to this particular problem. I can’t be the only one to ever have had this issue – is this some well kept mycologist secret method to ITS success? My hope is that by writing this blog post, I can save others from weeks (or months) of mental anguish over poor quality ITS taxonomic classification when the answer is hidden (or not so hidden) away in a silly parameter file.