Category Archives: ecology

Postdoc: Fungal Ecology and Evolution

Postdoctoral Position in Fungal Ecology and Evolution

Contact: Dr. Serita Frey, Department of Natural Resources & the Environment,University of New Hampshire, Durham, NH USA

(Questions can also be directed to Dr. Anne Pringle, University of Wisconsin-Madison,

Our laboratory aims to understand connections between microbial community structure and ecosystem function. We document the impacts of environmental change on the diversity, community composition, and function of the soil microbial community, and test whether shifts in the community subsequently influence ecosystem-scale carbon and nutrient cycling dynamics. A recent focus is on anthropogenic drivers of fungal evolution, in collaboration with Dr. Anne Pringle at the University of Wisconsin.

This two-year position will focus specifically on fungal evolution within global change contexts, with an emphasis on how fungi evolve in response to soil warming and simulated nitrogen deposition. The candidate will have the flexibility to explore questions that fall within this general topic area, while building on previous research conducted in the Frey and Pringle Labs. The candidate is expected to have strong interests and experience in evolution and ecology. Expertise in cultivation-based and genomic analyses as applied to soil fungi is highly desirable. The candidate will be expected to work independently, but also cooperatively with other members of the lab and with the Pringle Laboratory. A Ph.D. degree in evolution, ecology, natural resources, microbiology, or related field, along with relevant research experience is required. The target start date is Oct. 1, 2017, though an earlier start date is possible. Review of applications will begin April 15, 2017 and continue until the position is filled.

To apply please send the following items in a single PDF file to Serita Frey ( letter of interest/experience, CV, and the names and contact information of three professional references.

Reference ITSdb for QIIME released

Good news!

An alpha version of the ITS reference database for use with QIIME was released this week as part of the QIIME team development. There are more details on the release and how to obtain it from the project’s post here.

Please note that this is an Alpha release and may not be completely consistent, but the team wants to make something available now to give people a starting DB for use of QIIME and ITS data.  Parameters will need to be modified from the defaults, so watch the QIIME space, and we are working on a best practices document in the lab here to help ease the training in this.

All the data are also in a github repository and this is built starting from the database provided and curated by the UNITE team. I love that the data are getting version controlled here so it is easy to look at versions and revisions.

Fungal ITS database workshop

We held a workshop last for some of the people using and developing tools and databases for fungal molecular ecology in Fungi in (beautiful) Boulder, CO last week as part of our efforts in the Microbiome of the Built Environment. I am working with my co-organizer and participants to prepare a meeting summary and some descriptions of concrete plans forward. We expect this to make it easier to analyze Fungal ITS sequences in tools like QIIME and provide linkage with resources built around phylogenetic analyses of Fungi. An longer meeting summary will be posted in the coming week after we have all the presentations gathered and the details of the meeting written out.


Distribution of fungal ITS sequences in GenBank

As part of background in preparing a grant I ended up writing a few scripts to see the distribution of fungal species with ITS data in GenBank.  The whole spreadsheet of the data is public and available here and I walk you through the data generation and summary below.

ITS (Internal Transcribed Spacer) is the typically used barcode for identifying fungi at the species level as it works for most (but not all) groups of Fungi. It falls between highly conserved nuclear rDNA genes (18S, 5.8S, 28S) but tends to be hypervariable making it a reasonable locus for identification of species since it tends to be unique between species but fairly unchanged among individuals from the same species. You can see a Map of the amplified region from Tom Brun’s site or info at Rytas Vilgalys’s site among others.

The script to extract these and dump the numbers from GenBank uses Perl, BioPerl, and is plotted in a Google docs table. I queried for all ITS sequences with a pretty simple query – some people use a better more thorough query to get the list of GIs so I separated the GI query from the statistics about taxonomy.

The GI query code uses BioPerl and queries GenBank over the web to dump out a file of GI numbers  The code is in this Perl script.

This generates a file with GI (genbank identifiers) numbers for nucleotide records. This is not cleaned up to remove problematic seqs, but since we’re interested in overall statistics, I don’t think is that important if there are some records with problem.  You might want to do some cleanup of these data and expand the query before using it as a reference ITS database for your BLAST queries. See tools built by Henrik Nilsson and others like Emerencia for some of the cleanup and detection of problems with a dataset like this of ITS.

But given a list of GIs from any query – in our case of ITS sequences – what is the distribution of taxa (based on what is specified by the submitted which is not always correct!)? Of course some aren’t specified to the species level or even to the genus level so the code has to be smart enough to put those in a different category.  But of those specified to a particular taxonomic level – what are they?  This script tallies the information about the phyla and genus and dumps them out – it takes a while to run the first time because it must build a database for all the GI to taxon record links (gi_to_taxa_nucl.dmp file from ncbi taxonomy) so be prepared to wait a while and dedicate several dozen gigabytes to get this all working the first time.

So what is the most abundant deposited genus?  Well according to this analysis it is Fusarium. Which are found everywhere especially in soil. This distribution may have much more to do with the types of places being sampled and the types of questions researchers are working on rather than about relative abundance worldwide so take it as an interesting observation of what is in the databases!  Only in particular environments with dedicated studies to fungal species (for example, the indoor environment or a particular area of a forest or fungi associated with trees in an urban and rural environment or one of many other studies not mentioned) can we really say something. What is important to note also is the massively parallel sequencing studies using 454 are coming online and not necessarily being dumped directly into this particular database at GenBank – these number represent the mainly Sanger clone sequenced data from years past, but it will be a whole new ball game in the next few years as studies start doing 454 sequencing as primary means to identify community structure.





click on image to see this in google docs spreadsheet





So who is generating all that data — well I wrote another version of the script which dumps out the authors for records from a particular taxa by querying the genbank record for the author field of all the records that came from a particular taxa.
The data are in this spreadsheet.

So a few bits of code using queries of GenBank and BioPerl to link things together, hope you see some sense of what is out there and maybe can think of interesting variations on this theme to address other data mining questions.

Amphibian skin bacteria shown to fight off Batrachochytrium dendrobatidis.

A year ago researchers at James Madison University discovered that, Pedobacter cryoconitis, a bacteria first found on the skin of red backed salamanders, was found to prevent the growth of the chytrid B. dendrobatidis, which is currently decimating frog populations.

(Mountain Yellow-Legged Frog from wikipedia)

The newest research on the subject is being presented this year at ASM by Brianna Lam who worked with other biologists from both San Francisco State University and JMU.

Lam’s research indicates that adding pedobacter to the skin of mountain yellow-legged frogs would lessen the effects of Batrachochytrium dendrobatidis (Bd), a lethal skin pathogen that is threatening remaining populations of the frogs in their native Sierra Nevada habitats.

Lam first conducted petri dish experiments that clearly showed the skin bacteria repelling the deadly fungus. She then tested pedobacter on live infected frogs, bathing some of them in a pedobacter solution. The frogs bathed in pedobacter solution lost less weight than those in a control group of infected frogs that were not inoculated.

In addition to the lab experiments, the JMU and SFSU researchers have studied the yellow-legged frogs in their natural habitats and discovered that some populations with the lethal skin disease survive while others go extinct. The populations that survived had significantly higher proportions of individuals with anti-Bd bacteria. The results strongly suggest that a threshold frequency of individuals need to have anti-Bd bacteria to allow a population to persist with Bd. (from Eureka alert)

The research above is really interesting and I am curious as to how the bacteria is actually killing the chytrid. The only other research I can think of where chytrids were being killed was a BBC news article that wrote about scientists bathing frogs in chloramphenicol.

Microbial Ecology in Science

Science has a section dedicated to Microbial Ecology including a review describing microbial biogeography studying communities on the basis of trait rather than taxonomic diversity. Certainly this interlinks with metagenomic approaches well, something I’ve been thinking about more after visiting some of the folks at Montana State Thermal Biology Institute and all the increasingly massive datasets like what CAMERA provides.