I just want to wish everybody a merry Christmas and a happy new year, from the sunny town of Stellenbosch in South Africa. I will have the pleasure of spending Christmas here this year. As some holiday reading I have provided a longer peace on metagenomics, and I hope to be able to provide a shorter one on resistance genes as well. Happy holidays!
One thing that I find slightly annoying is when people do not get the basic concepts right – or when debatable concepts are used without discussion of their implications. This further annoys me when it is done by senior scientists, who should know better. Sometimes, I guess this happens out of ignorance, and sometimes to be able to stick your subject to a certain buzzword concept. Neither is good, even though the former reason is little more forgivable then the latter. One area where this problem becomes agonizingly evident is when molecular biologists or medical scientists moves into ecology, as has happened with the advent of metagenomics. When the study of the human gut microflora turned into a large-scale sequencing effort, people who had previously studied bacteria grown on plates started facing a world of community ecology. However, I get the impression that way too often these people do not ask ecologists for advice, or even read up on the ecological literature. Which, I suppose, is the reason why medical scientists can talk about how the human gut microflora can “evolve” into a stable community a couple years after birth, even though words such as “development” or “succession” would be much more accurate to describe this change.
The marker gene flaw
To set what I mean straight, let us compare the human gut to a forest. If an open field is left to itself, larger plants will slowly inhabit it, and gradually different species will replace each other, until we have a fully developed forest. Similarly, the human gut microflora is at birth rather unstable, but stabilizes relatively quickly and within a few years we have a microbial community with “adult-like” characteristics. To arrive at this conclusion, scientists generally use the 16S (small sub-unit) genetic marker to study the bacterial species diversity. This works in pretty much the same way as going out into the forest and count trees of different kinds.
Now, if I went out into the forest once and counted the tree species, waited for 50 years and then did the same thing again, I would presumably see that the forest species composition had changed. However, if I called this “evolution”, fellow scientists would laugh at me. Raspberry bushes do not evolve into birches, and birches do not evolve into firs. Instead, ecologists talk about “succession”; a progressive transformation of a community, going on until a stable community is formed. The concept of succession seems well-suited also to describe what is happening in the human gut, and should of course also be used in that setting. The most likely driver of the functional community changes is not that some bacterial species have evolved new functions, but rather that bacterial species performing these new functions have outcompeted the once previously present.
In fact, I would argue that it is impossible to study evolution through a genetic marker such as the 16S gene (except in the rare case when you study evolution of the 16S gene itself). Instead, the only thing we could assess using a marker gene is how the copy number of the different gene variants change over time (or space, or conditions). The copy number tells us about the species composition of the community at a given time, which can be used to measure successional changes. However, evolutionary changes would require heritable changes in the characteristics of biological populations, i.e. that their genetic material change in some way. Unless that change happens in the marker gene of choice, we cannot measure it, and the alterations of composition we measure will only reflect differences in species abundances. These differences might have arisen from genetic (i.e. evolutionary) changes, but we cannot assess that.
What are we studying with metagenomics?
This brings us to the next problem, which is not only a problem of semantics and me getting annoyed, but a problem with real implications. What are we really studying using metagenomics? When we apply an environmental sequencing approach to a microbial community, we get a snapshot of the genetic material at a given time and site; at specific conditions. Usually, we aim to characterize the community from a taxonomic or functional perspective, and we often have some other community which we want to compare to. However, if we only collect data from different communities at one time point, or if we only study a community before and after exposure, we have no way of telling if differences stem from selective pressures or from more a random succession progress. As most microbial habitats are not as well studied as the human gut, we know little about microbial community assembly and succession.
Also, in ecology a disturbance to a particular community is generally considered as a starting point for a new succession process. This process may, or may not, return the community to the same stable state. However, if the disturbance was of permanent nature, the new community will have to adapt to the new conditions, and the stable state will likely not have the same species distribution. Such an adaption could be caused by genetic changes (which would clearly be an evolutionary process), or by simple replacement of sensitive species with tolerant ones. The latter would be a selective process, but not necessarily an evolutionary one. If the selection does not alter the genetic material within the species, but only the species composition, I would argue that this is also a case of succession.
Complications with resistance
This complicates the work with metagenomic data. If we study antibiotic resistance genes, and say that bacteria in an environment have evolved antibiotic resistance, we base that assertion on that genes responsible for resistance have either evolved within the present bacteria, or have (more likely) been transferred into the genomes of the bacteria via horizontal gene transfer. However, if the resistance profile we see is simply caused by a replacement of sensitive species with resistant ones, we have not really discovered something new evolving, but are only witnessing spread of already resistant bacteria. In the gut, this would be a problem by itself, but say that we do the same study in the open environment. We already know that environmental bacteria have contained resistance genes for ages, so the real threat to human health here would be a spread from naturally resistant bacteria to human pathogens. However, as mentioned earlier, without extremely well thought-through methodology we cannot really see such transmissions of resistance genes. Here, the search for mobile elements, and large-scale takes on community composition vs. resistance profiles in contaminated and non-polluted areas can play a huge role in shedding light on the question of spreading. However, this will require larger and better planned experiments using metagenomics than what is generally performed at the moment. The questions of microbial community assembly, dispersal, succession and adaption are still largely unanswered, and our metagenomic and environmental sequencing approaches have just started to tinker around with the lid of the jar.
I am extremely happy to announce that Metaxa 1.1 (first announced back in July) has finally left the beta stage, and is now designated as a feature complete 1.1 update. We consider this update stable for production use. The 1.1 update utilize hmmsearch instead of hmmscan for higher extraction speeds and better accuracy. This clever trick was inspired by a blog post by HMMER’s creator Sean Eddy on hmmscan vs hmmsearch (http://selab.janelia.org/people/eddys/blog/?p=424). As the speedup comes from the extraction step, the speed increase will be largest for huge data sets with only a small proportion of actual SSU sequences (typically large 454 metagenomes).
What took so long, you might ask, as I promised an imminent release already in August. Well, during testing a difference in scoring was discovered. This difference did not have any implications for long sequences (> ~350 bp), but caused Metaxa to have problems on short reads (most evident on ~150 bp and shorter). Therefore, the scoring system had to be redesigned, which in turn required more extensive testing. Now, however, Metaxa 1.1 has a fine-tuned scoring system, which by default is based on scores instead of E-values, and in some instances have even better detection accuracy than the old Metaxa version. We encourage everyone to try out this new version of Metaxa (although the 1.0.2 version will remain available for download). It should be bug free, but we cannot ensure 100% compatibility in all usage scenarios. Therefore, we are happy if you report any bugs or inconsistencies to the e-mail address: metaxa (at] microbiology [dot) se.
The new version of Metaxa can be downloaded here: http://microbiology.se/software/metaxa/ Please note that the manual has not yet been updated yet, so use the help feature for the up-to-date options. Happy SSU detecting!
I usually try not to be too personal on this site, but to stay more scientific in my tone and scope. However, today’s news that Steve Jobs have passed away inclines me to make a short comment. My dad brought home a Macintosh Plus in 1989. Since then, I have without any breaks been a Mac user. I did my first programming attempts around the age of nine in HyperCard. In short, without the Mac I probably wouldn’t have ended up where I am today. And without Steve, there would have been no Mac. Steve’s vision on ease-of-use computers helped me getting into the programming bandwagon in the early nineties. And even though my computer use has changed a lot since, I still feel hugely indebted to Steve Jobs for bringing the computing revolution into my living room. Thanks for the ride, Steve.
The 11th annual meeting of PhD students and Postdoc researchers in Bioinformatics in Sweden will take place in Lund the 29-30 September. The workshop is an opportunity for young researchers to meet, exchange ideas, and keep up to date with the growing body of knowledge. I will go there, and you should be there too! Besides, it’s free for PhD students and Postdocs! All info can be found at the workshop website. The last time I was there (2010) really fueled some interesting discussions, and I am really looking forward to the event this year. Hope to meet you there, fellow Swedish bioinformaticians!
Finally, the Metaxa FAQ is ready! If you have any other questions, please mail them to metaxa [at] microbiology [dot] se, and I will include them in the FAQ at some later point. I would like to thank anyone who has contributed with questions, suggestions, comments and other types of feedback so far. It really helps improving this software. The FAQ is found here.
You may also wonder what has happened to the stable version of the 1.1 Metaxa speedup I promised in July. It is still on the way, but due to a minor computer failure and other CPU-heavy tasks being of higher priority the software still has not been fully tested. As we want to release a truly stable and functional update, we need to hold back on the package for some more time. Be patient, or try out the beta that is already available.
Phil Goetz at JCVI recently posted his reflections from the Summit of Systems Biology. I was not there, but I read his summary with interest. Now, what strikes me as interesting is the notion that “there were no talks on metagenomics. This also struck me as odd; bacterial communities seem like a natural systems biology problem.” Having been working with microbial communities for a while, I am surprised that the modeling perspective that is so prevalent in macro-organism ecosystems ecology have not yet really come to fruition in microbial ecology. With the tremendous amounts of sequences that are pouring over us from microbial communities, and with the plethora of functional metagenomics annotation that is made, how come that there has been so little research in the actual interactions between microorganisms within e.g. biofilms?
The problem is also connected to the lack of time-series data from community research. To be able to understand how a system behaves under changing conditions, we need to measure its reactions to various parameter changes over time. Instead of pooling metagenomes to reduce temporal “noise” we need to be better at identifying the changing parameters and then use the temporal differences to look for responses to the parameter changes. By applying a functional metagenomics perspective at each sample point, combining this with measured changes in community species structure (as measured e.g. by 16S or some other marker gene), and correlating this with changes in the parameters, we should be able to build a model of how the ecosystem responds to changing environments. With the large-scale sequencing technologies available today, and the possibilities given by metatranscriptomics, these ideas should be challenging but not impossible.
I am not saying that any of these things have not been done. But it has been done to a surprisingly small extent. I would highly appreciate reading a paper trying to build a mathematical model of how the ecosystem functions in bacterial communities shift in response to an environmental stressor. Because when someone builds such a model we suddenly have a tool to take microbial community research from an explorative perspective to an applied one. The applied perspective will be useful for actually protecting environments and ecosystem services, as well as for understanding how to manipulate microbial ecosystems to maximize the outtake beneficial to society. Also, the understanding the ecosystem dynamics of microbial systems could be carried over to macro-ecosystems and provide a small-scale ecosystem laboratory for all ecosystem research. Such a shift towards applied microbial community systems biology will be more or less necessary to be able to argue for more resources and time being spent on e.g. metagenomics. And I believe that we will soon be there, because the step is shorter than we might imagine.
I’m working on an update to Metaxa that will bring at least double speed to the program (and even more when run on really large data sets on many cores). While there is still no real release version of this update (version 1.1), I have today posted a public “beta”, which you can use for testing purposes. Do not use this version for anything important (e.g. research) as it contains at least one known bug (and maybe even more I haven’t discovered yet). I would appreciate, if you are interested, that you download this version and e-mailed any bugs or inconsistencies found to me (firstname.lastname[at]microbiology.se).
Note that to install this version, you first need to download and install the current version of Metaxa (1.0.2). Then the new version can be used with the old’s databases.
One potential use for Metaxa (paper) is to include it in a pipeline for classification of SSU rRNA in metagenomic data (or other environmental sequencing sets). However, as Metaxa is provided from this site, it only classifies SSUs to the domain level (archaea, bacteria and eukaryotes, with the addition of chloroplasts and mitochondria). It is also able to do some (pretty rough) species guesses using the “
--guess_species T” option. An easy solution to implement would be to pass the Metaxa output, e.g. “metaxa_output.bacteria.fasta” to BLAST, and compare all these sequences to the sequences in e.g. the SILVA or GreenGenes database. There is, however, a way to improve this, which uses Metaxa’s ability to compares sequences to custom databases. In this tutorial, I will show you how to achieve this.
Before we start, you will of course need to download and install Metaxa, and its required software packages (BLAST, HMMER, MAFFT). When you have done this, we can get going with the database customization. I will in this tutorial use the SILVA database for SSU classification. However, the basic idea for the tutorial should be easily applicable to GreenGenes and other rRNA databases as well.
- Visit SILVA through this link, and download the file named “SSURef_106_tax_silva.fasta.tgz”. The file is pretty big so it may take a while to download it. If you’re running Metaxa on a server, you’ll have to get the SILVA-file to the server somehow.
- Unzip and untar the file (Mac OS X makes this neatly by doubleclicking the file, on linux you can do it on the command line by typing “
tar -xvzf SSURef_106_tax_silva.fasta.tgz“). This will give you a FASTA-file.
- The FASTA-file needs to be prepared a bit for Metaxa usage. First, we need to give Metaxa identifiers it can understand. Metaxa identifies sequences’ origins by the last character in their identifier, e.g. “>A16379.1.1496.B”. Here, “.B” indicates that this is a bacterial sequence. We are now going to use the unix command sed to process the file and insert the appropriate identifiers.
- We begin with the archaeal sequences. To get those straight, we type:
sed "s/ Archaea;/.A - Archaea;/" SSURef_106_tax_silva.fasta > temp1
Notice that we direct the output to a temporary file. It is bad practice to replace the input file with the output file, so we work with two temp-files instead.
- The next step is also easy, now we find all eukaryote sequences and add E:s to the identifiers:
sed "s/ Eukaryota;/.E - Eukaryota;/" temp1 > temp2
- Now it becomes a little more complicated, as SILVA classes mitochondrial and chloroplast SSU sequences as subclasses of bacteria. However, there is a neat little trick we can use. First we do the same with the bacterial sequences as with the archaeal and eukaryote:
sed "s/ Bacteria;/.B - Bacteria;/" temp2 > temp1
- Now, we can use two a little more complicated commands to annotate the mitochondrial and chloroplast sequences:
sed "s/\.B - \(Bacteria;.*;[Mm]itochondria;\)/.M - \1/" temp1 > temp2
sed "s/\.B - \(Bacteria;.*;[Cc]hloroplast;\)/.C - \1/" temp2 > temp1
- We also need to get “rid” of the unclassified sequences, by assigning them to the “other” origin (O):
sed "s/ Unclassified;/.O - Unclassified;/" temp1 > temp2
- We begin with the archaeal sequences. To get those straight, we type:
- That wasn’t too complicated, was it? We can now check the number of different sequences in the file by typing the pretty complicated command:
grep ">" temp2 | cut -f 1 -d " " | rev | cut -f 1 -d "." | sort | uniq -c
If you have been working with the same files as me, you should now see the following numbers:
- At this stage, we need to remove the full taxonomy from the FASTA headers, as Metaxa cannot handle species names of this length. We do this by typing:
sed "s/ - .*;/ - /" temp2 > temp1
- We can now change the temp-file into a FASTA file, and delete the other temp-file:
mv temp1 SSURef.fasta
- We now need to configure Metaxa to use the database. First, we format a BLAST-database from the FASTA-file we just created:
formatdb -i SSURef.fasta -t "SSURef Metaxa DB" -o T -p F
- With that done, we can now run Metaxa using this database instead of the classification database that comes with the program. By specifying that we want to guess the species origin of sequences, we can get (as accurate as SILVA lets us be) which species each sequence in our set come from. We do this by using the
metaxa -i test.fasta -d SSURef.fasta -o TEST --guess_species T --cpu 2
The input in this case was the test file that comes with Metaxa. Note also that we’re using two CPUs to get multithreaded speeds. Remember that you must provide the full (or relative) path to the database files we just created, if you are not running Metaxa from the same directory as the database resides in.
- The output should now look like this (taken from the bacterial file):
>coryGlut_Bielefeld_dna Bacterial 16S SSU rRNA, best species guess: Corynebacterium glutamicum
>gi|116668568:792344-793860 Bacterial 16S SSU rRNA, best species guess: Arthrobacter sp. J3.40
>gi|117927211:c1399163-1397655 Bacterial 16S SSU rRNA, best species guess: Acidothermus cellulolyticus
And so on. As you can see the species names are now located at the end of each definition line, and can easily be extracted using sed, e.g. “
grep ">" TEST.bacteria.fasta | sed "s/.*: //"“.
And that’s it. It’s pretty simple, and can easily be scripted. In fact, I have already made the bash script for you. That means that the short version is, download the script, download the sequence file from SILVA, move into the directory you have downloaded the file to and run the script by typing:
A few notes at the end. The benefit of using this approach is that we maintain the sorting capabilities, marking of uncertain sequences and error checking of Metaxa, but we don’t have to add another BLAST step after Metaxa has finished. However, as this database we create is a lot bigger than the database that comes with Metaxa, the running time of the classification step will be substantially longer. This is in most cases acceptable, as that time is the same as the time it would have taken to run BLAST on the Metaxa output. It should also be noted that this approach limits Metaxa’s ability of classifying 12S sequences, as there are no such sequences in SILVA. Good luck with classifying your metagenome SSUs (and if you use Metaxa in your research, remember to cite the paper)!
It seriously worries me that a number of indications recently have pointed to that the heavy use of antibiotics does not only drive antibiotic resistance development, but also the development towards more virulent and aggressive strains of pathogenic bacteria. First, the genome sequencing of the E. coli strain that caused the EHEC outbreak in Germany in May revealed not only antibiotic resistance genes, but also is also able to make Shiga toxin, which is causes the severe diarrhoea and kidney damage related to the haemolytic uremic syndrome (HUS). The genes encoding the Shiga toxin are not originally bacterial genes, but instead seem to originate from phages. When E. coli gets infected with a Shiga toxin-producing phage, it becomes a human pathogen . David Acheson, managing director for food safety at consulting firm Leavitt Partners, says that exposure to antibiotics might be enhancing the spread of Shiga toxin-producing phage. Some antibiotics triggers what is referred to as the SOS response, which induces the phage to start replicating. The replication of the phage causes the bacteria to burst, releasing the phages, and with them the toxin .
Second, there is apparently an ongoing outbreak of scarlet fever in Hong Kong. Kwok-Yung Yuen, microbiologist at the University of Hong Kong, has analyzed the draft sequence of the genome, and suggests that the bacteria acquired greater virulence and drug resistance by picking up one or more genes from bacteria in the human oral and urogenital tracts. He believes that the overuse of antibiotics is driving the emergence of drug resistance in these bacteria .
Now, both of these cases are just indications, but if they are true that would be an alarming development, where the use of antibiotics promotes the spread not only of resistance genes, impairing our ability to treat bacterial infections, but also the development of far more virulent and aggressive strains. Combining increasing untreatability with increasing aggressiveness seems to me like the ultimate weapon against our relatively high standards of treatment of common infections. Good thing hand hygiene still seems to help .
- Phage on the rampage (http://www.nature.com/news/2011/110609/full/news.2011.360.html), Published online 9 June 2011, Nature, doi:10.1038/news.2011.360
- Mutated Bacteria Drives Scarlet Fever Outbreak (http://news.sciencemag.org/scienceinsider/2011/06/mutated-bacteria-drives-scarlet.html?etoc&elq=cd94aa347dca45b3a82f144b8213e82b), Published online 27 June 2011.
- Luby SP, Halder AK, Huda T, Unicomb L, Johnston RB (2011) The Effect of Handwashing at Recommended Times with Water Alone and With Soap on Child Diarrhea in Rural Bangladesh: An Observational Study. PLoS Med 8(6): e1001052. doi:10.1371/journal.pmed.1001052 (http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.1001052)