This week there’s an actual call to action in the tweets. See Steven Salzberg’s tweet about the 2017 Service to America award and GenBank is nominated–you can vote every day! The rest of the week’s intriguing tweets include yeast diversity, British historical diversity, non-coding regulatory stuff, computer-coding DNA hackers, and setting standards for information of uncultured samples of organisms and for personal data.
Welcome to our Friday feature link collection: SNPpets. During the week we come across a lot of links and reads that we think are interesting, but don’t make it to a blog post. Here they are for your enjoyment…
I know a lot of sci-geeks were lured into the field by space. Rockets, space travel, great science fiction–I understand the appeal. But as it seemed much more physics oriented, space wasn’t my primary fascination. But the effects of space travel on organisms (humans, of course, but others as well)–that’s something I can find pretty intriguing. And I was always really pleased to hear about the various cells and organisms that were on the trips and undergoing experimentation.
But until last week, had never gone to the primary literature that resulted from these studies to have a look. I had seen an article in Wired that talked about the work, though, and I had to see it. Have a look at the piece How Plants Deal With Space Travel for their take on it. Coincidentally on Fascination of Plants Day a piece appeared in SciAm as well, on other Plants! In! Space! (Moon trees were totally new to me in that piece, but I was previously familiar with Space Beer barley.)
What does happen to gene expression in space? It changes in some expected ways: the structural features of the cytoskeleton can be affected, as can metabolism. These had been previously described. But with new technologies now they can look more broadly across the genome to see further details.
This is not your average experiment, though. One set needs to be run on the ground as a control, while the other one needs to be loaded on to spacecraft, monitored for all sorts of external conditions, and run in the shuttle. They spent a bit of time explaining how they coordinated those to be sure they were making as direct comparisons as possible. But they get all that under control, and have a look at the Arabidopsis plants in a couple of ways.
They have seedlings to look at, but they also used tissue culture cells. I think that’s an interesting thing to do–it gets at some of the differences between structural features that might be more affected by gravity in an intact plant vs. a dish. And you can also think about the differences in tissue types.
The space seedlings had a lot of upregulation of pathogen/wound response genes. Other stress-response genes were up as well. Downregulated genes were curious to me: a lot of transcription factors went down, but cell wall metabolism and elongation ones did too. I might have expected the cell walls to have to try harder if the cytoskeleton was misbehaving. Gravitropism genes were down, which seems sensible.
The cultured cells were different. They ramped up their heat shock genes more, but also did have some stress-responses for wounding and other conditions too. But of course, all of the scenarios included lots of things that would have to be characterized in more detail to fully understand. The general features are interesting and informative, but some specific genes might provide interesting clues too.
One of the things that struck me was that the tissue cultures cells could be more prepared to just throw some “on” switches because they are more undifferentiated–or at least not under the same tissue-specific constraints that seedlings are. That could explain some of the transcription factor differences. And they go into that a bit in the discussion.
They go into more details and offer lists of the genes to examine, and in the discussion they speculate about some of the differences between the conditions. But I imagine it’s important to examine both scenarios. If I was on a long space flight I’d want some fresh veggies to eat, but maybe also some cultured cells in a vat to produce various things–including oxygen.
I don’t imagine this information is anything I need to know soon–I have no plans for flight myself, although I think recently Trey bought a lottery ticket for that… I’m glad someone is looking at it.
When I contacted the team with a question about the paper, Anna-Lisa Paul also pointed me to this video. You can see her trying to work on a sample project like it would happen in space. Funny, just the other day I posted about that guy who needed to get genome samples from dangerous critters–now there’s this. Genomics can be a lot more physically challenging than you might think–it’s not all done on keyboards!
Reference: Paul, A., Zupanska, A., Ostrow, D., Zhang, Y., Sun, Y., Li, J., Shanker, S., Farmerie, W., Amalfitano, C., & Ferl, R. (2012). Spaceflight Transcriptomes: Unique Responses to a Novel Environment Astrobiology, 12 (1), 40-56 DOI: 10.1089/ast.2011.0696
In this week’s tip I’d like to introduce you to CircuitsDB, which describes itself as:
“…a database where transcriptional and post-transcriptional (miRNA mediated) network information is fused together in order to propose and recognize non trivial regulatory combinations. “
I found out about the database from the BioMed Central article “CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse“, which I cite below. I had already been thinking about miRNAs because I am slated to update our miRBase tutorial in the near future and have been reading/catching up on the latest in the field. The CircuitsDB paper by Olivier Friard et al does a really nice job of quickly and clearly laying out the background of the project – how transcription factors have long been studied for their transcriptional regulation of protein-coding genes involved in any manor of pathways, including those of disease. It goes on to describe that the study of microRNAs, or miRNAs, is a newer field studying the post-translational regulatory effects of miRNAs on protein-coding genes and their functions. Current efforts are moving to integrate the two areas of research to create more complete, and admittedly more complex, regulatory views of protein-coding genes and the affects on disease and other pathways.
The developers of CircuitsDB also very clearly describe how they have mined, analyzed and connected data from several top databases – many of which we have tutorials on, such as OMIM, miRBase, Ensembl and others – in order to create feed-forward regulatory loops, or FFLs, of TFs, affected miRNAs and ultimately affected protein-encoding genes. The image at the right is from their original paper: “Genome-wide survey of microRNA–transcription factor feed-forward regulatory circuits in human” (cited below), which reported the development of the computational framework for the mixed miRNA/TF Feed-Forward regulatory circuits that are freely available through the CircuitsDB web interface. This original paper is available for free, with registration to RSC Publishing, and provides a detailed description of their original development, as well as access to several supplemental files.
Essentially networks linking transcription factors and affected genes, miRNAs and affected genes, and transcription factors and miRNAs were painstakingly connected through an ab-initio oligo analysis. Support was then gained for the connections by analyzing enriched GO terms, disease connections, and previously-known connections found in other specialized resources. The CircuitsDB interface offers multiple tools. The main tool (FFL) is what I show in this tip & is the tool that searches for the networks diagrammed above. The MYC FFL is an impressive “curated database of miRNA mediated Feed Forward Loops involving MYC as Master Regulator”, and includes information on the direction of regulation, loop participants, evidence levels and more. The Transcriptional network tool allows a user to search with either a miRNA & find its regulating TF, or search with a TF & find regulated genes or miRNAs. The Post-transcriptional network tool is similar, but allows searches for a miRNA or gene to find regulated genes or regulating miRNA, respectively. So check out the tip & then check out CircuitsDB – enjoy!
References: Friard, O., Re, A., Taverna, D., De Bortoli, M., & Corá, D. (2010). CircuitsDB: a database of mixed microRNA/transcription factor feed-forward regulatory circuits in human and mouse BMC Bioinformatics, 11 (1) DOI: 10.1186/1471-2105-11-435
Re, A., Corá, D., Taverna, D., & Caselle, M. (2009). Genome-wide survey of microRNA–transcription factor feed-forward regulatory circuits in human Molecular BioSystems, 5 (8) DOI: 10.1039/B900177H
I’m going to admit, I know little of acetylation as a regulatory mechanism, though after reading through the paper, I found this quite and interesting find and it suggests to me that genomics has a lot to offer in the advance in our understanding of regulation and evolution.
Three things jumped out at me though.
The first is minor. The authors use the term Acytelome. You can now add that to the huge list of -omics terms to keep straight :D.
The second is that they use STRING to complete an analysis of networked interactions of the proteins discovered in their study and the processes where they are found, as you can see in their figure.
I did my postdoc and some later research in the lab (Peer Bork, EMBL) that developed STRING, and I’ve created a tutorial on it, so any time it’s used, I’m interested :D. So, I went to Methods and Materials to see how the analysis was done. Though there was a decent explanation of the process, it was not enough for me to recreate the analysis. This is not a criticism of the paper or the authors, but of how papers are being published. More and more, papers include genomics analysis, but rarely are these reported in the research paper in the detail needed to easily reproduce the analysis. Projects like Galaxy (publicly available tutorial) and Taverna are filling that void, so I’d like to see more Methods and Materials sections include analysis histories and workflows. It definitely would help in the advancement of science.