I am preparing an introduction-to-fungi class. Found this cool Aspergillus-related animation:
Life on Earth never fails:
Not sure what this is:
Of course, there are the zombie ants I have written about before:
The first bioinformatics meeting I went to was in 1996 at the Nachsholim resort, north of Tel Aviv. I received a fellowship for the duration, and shared a room with the brilliant Golan Yona, then a grad student at the Hebrew University. I was doing biochemistry at the time and knew next to nothing about bioinformatics, except that it seemed like an interesting thing to get into if you liked biology and programming. The meeting was great: Samuel Karlin, Pavel Pevzner, Dannie Durand, Temple Smith and Eugene Myers were there. Lots of down time on the beach and in the pub by the beach. I learned an incredible amount in four days and by the time the meeting ended, I was hooked. I wrapped up my grad school work in biochemistry as a Master’s degree, and joined Hanah Margalit’s lab for a PhD in bioinformatics.
Dan Graur gave a talk at that meeting on The One True Phylogenetic Tree of Mammals. Dan’s talks are fast and funny. His tactic of building audience interest is by making them think they are missing something great if they even dare blink when he is talking; it works. Dan was complaining that all genomic efforts were invested in inconsequential organisms such as humans, mice and Drosophila, and no one was interested in the Aardvark or Sloth genomes. He bemoaned the situation, as he needed the Aardvark, and a few thousand other mammalian species to get the “One True Tree”. Later that day, over dinner, Pavel Pevzner suggested sequencing the X chromosome from all mammals using the then-new DNA chip technology. The X chromosome being a “microgenome”, with no transposable elements from other chromosomes, making it a perfect candidate for being a proxy for a genome.
In 1996, capillary sequencing was well established, but still quite expensive,usable only by large institutions and companies. DNA chips, however, were thought to become the next cheap sequencing technology, and there were many expectations that they would enable mass genomics. Chips turned out to be useful in many other applications, but not in mass sequencing. We had to wait almost 10 years for pyrosequencing and other cheap mass sequencing technologies to hit the scene.
The cost of sequencing is still dropping exponentially, so fulfilling Dan’s wishes is very much in the making now. We are getting closer to getting the genomes, not only of all mammals, but of all vertebrates. The Genome 10K initiative was officially launched in April 2009. Today, the paper describing the project has been published in the Journal of Heredity. The goal is to collect and systematically sequence 10,000 vertebrate (not just mammalian) genomes. 10,000 is a nice round number, but looking at the paper, their actual aim is 16,203. Wow! That includes some recently extinct species for which genomic material may still be obtained like the Tasmanian Wolf.
Entry of the Animals Into Noah's Ark / Jan Breughel the Elder
Note that they do not plan to begin sequencing immediately. The cost of sequencing is still too high, and they are still waiting for costs to decrease to $2500 per genome, which is one-hundred times cheaper than it is today. But at the rate cost is dropping, they estimate that mass sequencing can be started in a few years. In the meantime, they are soliciting samples from the community.
A lot of effort for the True Tree… but it’s not only for that. It is the next logical step to take after completing the genome of a few select organisms. The library of life. To achieve an understanding of animal evolution on a level that in 1996 we could only joke about. More information can be found on their site. Here is the closing paragraph from the article:
As the printing of the first book by Johannes Gutenberg altered the course of human history, so did the human genome project forever change the course of the life sciences with the publication of the first full vertebrate genome sequence. When Gutenberg’s success was followed by the publication of other books, libraries naturally emerged to hold the fruits of this new technology for the benefit of all who sought to imbibe the vast knowledge made available by the new print medium. We must now follow the human genome project with a library of vertebrate genome sequences, a genomic ark for thriving and threatened species alike, and a permanent digital record of countless molecular triumphs and stumbles across some 600 million years of evolutionary episodes that forged the “endless forms most beautiful” that make up our living world.
. (2009). Genome 10K: A Proposal to Obtain Whole-Genome Sequence for 10 000 Vertebrate Species Journal of Heredity DOI: 10.1093/jhered/esp086
Check Hayden, E. (2009). 10,000 genomes to come Nature, 462 (7269), 21-21 DOI: 10.1038/462021a
For those who are not in the structural biology community: Warren DeLano wrote and maintained PyMol, the software of choice for molecular visualization. Practically anyone who published anything requiring a biomolecular image used PyMol. It is a great piece of software, powerful and extensible. Warren was strongly committed to writing quality product that served the community well. He was also strongly committed to maintain an open source licence for PyMol. This must be one of the saddest emails I have ever received:
Dear CCP4 Community:
I write today with very sad news about Dr. Warren Lyford DeLano.
I was informed by his family today that Warren suddenly passed
away at home on Tuesday morning, November 3rd.
While at Yale, Warren made countless contributions to the computational tools
and methods developed in my laboratory (the X-PLOR and CNS programs),
including the direct rotation function, the first prediction of
helical coiled coil
structures, the scripting and parsing tools that made CNS a universal
computational
crystallography program.
He then joined Dr. Jim Wells laboratory at USCF and Genentech where he pursued
a Ph.D. in biophysics, discovering some of the principles that govern
protein-protein interactions.
Warren then made a fundamental contribution to biological sciences by
creating the
Open Source molecular graphics program PyMOL that is widely used throughout
the world. Nearly all publications that display macromolecular
structures use PyMOL.
Warren was a strong advocate of freely available software and the Open Source
movement.
Warren’s family is planning to announce a memorial service, but
arrangements have
not yet been made. I will send more information as I receive it.
Please join me in extending our condolences to Warren’s family.
Sincerely yours,
Axel BrungerAxel T. Brunger
Investigator, Howard Hughes Medical Institute
Professor of Molecular and Cellular Physiology
Stanford University
I didn’t write this one, but I wish I did. I found it on Science after Sunclipse. I guess that a CC license can be safely applied to anonymous chain letters.
Today CBSG continues with its pointers for budding scientists with the second part on serving as a peer reviewer for papers and grants.
Okay, you’ve decided that you are going to reject a manuscript. The naive reviewer might think that it is enough to simply state the reasons for the rejection as clearly and succinctly as possible. But this overlooks a major issue: ensuring that the authors do not know that it is you who rejected the manuscript.
Because the peer review process is anonymous, this may seem like no concern, as long as you extirpate all references to your own work to keep your identity secret. Wrong! You have to keep in mind that no matter how crappy the paper is, the authors are going to be pissed that it is rejected, and they are going to immediately begin wracking their brains to identify referees who might have done the dirty on them. Most will form a list of at least 5 or 6 people that they think are likely to have screwed them. Since most papers are reviewed by no more than 2-3 reviewers, this means you have a good chance of being on the list even if you were NOT the reviewer. Thus, particular pains must be taken to direct the authors ire elsewhere. Several different means to accomplish this are described below:
1. Pretend that you are British. (Note — this does not work well if you actually are British).
Just a few decades ago, it was enough to include a liberal sprinkling of “rathers” and “doubtlesses” throughout the review, and convert all colors to colours, analyze to analyse, polymerize to polymerise, etc. However, the increasing intellectual and cultural cross-pollination brought by the internet has rendered such limited measures ineffective. Thus, you need to be au courant with all the most specific idioms available to the average Brit.
For example, you might want to refer to a poorly run gel as being “dodgy”, “gammy” or “a bit pear-shaped”. Especially effective are slang terms derived from cricket. This is because no self-respecting American knows anything about this sport (indeed, outside the British Commonwealth, cricket is universally reviled as the one sport even more boring than baseball). Here are some cricket-based phrases worked into sentences that you might include in a review. Instead of writing “Some of the data presented by the authors are mutually contradictory” write “The authors seem to have gotten themselves into a bit of a sticky wicket”.
Instead of writing “The documentation of morpholino efficacy by monitoring expression of exogenously provided target rather than the endogenous target is not quite fair” write “Using GFP-ponticulin as a read out for the morpholino effects is not quite cricket”. And, instead of writing “I was chagrined to see that the authors ignored the previous studies by the Jones lab”, write “the failure of the authors to cite the seminal studies of Jones and colleagues hit me for six”.
1B. Pretend that you are an American pretending to be British (Note: this does work if you are British, but does not work if you are American.) The strategy here is similar to #1 above, but instead of being a little bit subtle, you go straight over the top. Thus, instead of writing “I seriously doubt that anyone will believe …”: “Blimey! Blokes would have to be right daft if they were to believe …”
2. Pretend that you are Canadian. This is harder because the only major language difference between Americans and Canadians is that the latter tend to mispronounce words with the short O sound such that they rhyme with newt. Needless to say, this sort of thing is not manifest in written reviews.
However, the canny reviewer can draw on the one or two features of Canadian culture that are unique. Interestingly (in light of the cricket discussion above) most of these revolve around Canadian football. For example, you might allude to a paper not being ready for the Grey Cup yet (a reference to the Canadian equivalent of the Super Bowl), describe an experimental situation as being “3rd and long” (an allusion to the fact that there are only three downs in Canadian football) or argue that the authors need to “bring in a couple more coaches” (referring to the fact that Canadian football teams have 4 head coaches). Cite obscure Canadian journals: “J Can. Med. Assoc.” or “Can. J. Cardio.” No one outside of Canada reads these journals.
3. Pretend that you are German. This is even harder, because even if you know some German, you have to write your review in English for most journals. Be extremely precise and technical. You could also try simply putting the verb at the end of your sentences (as in “The experiments in figures 5 and 6 should repeated be”), however this runs the risk of having yourself labeled not as a German, but as an imbecile or an incarnation of Yoda. Alternatively cite organic chemistry articles from the late 19th and early 20th century that have never been translated into English. Cite German aricles during the 30s and 40s when the rest of Academia was trying its best to ignore German science.
3B. Pretend that you are an American pretending to be German; sprinkle the text with flavorful comments such as “Ach mein lieber!” or “Du spinnst!” Or, if a line of reasoning is particularly awful, “Ist gibt ein Blutbat en der Hoelle!” Stick umlauts on random words, and make liberal use of the eszett. Downside: the editor will conclude you have flipped.
4. Pick one of the people from you own list of 5-6 enemies and pretend to be that person. Heavily cite their work. Reference their obscure conference presentations. Arrogantly suggest that person’s methods in favor of the methods used in the paper, especially where they are clearly inapplicable
One problem that I am facing is convincing colleagues of the utility of an Open Access publication. The usual arguments: more visibility, retention of the right to re-use material, the Greater Good, taxpayer access to taxpayer-funded research and so on don’t stick very well when faced with a $1500-$2500 or higher publication fee. These can be very big expenses if one is working on medium to small size grants, and where publication fees are sought, in part, from the College. Note: in many case the OA fees are not unaffordable; one would not request, in good faith, that the fees be waived or discounted by the publisher. But if one can use this money to pay the summer salary of a couple of more students, go to a conference, or upgrade / repair equipment, then the utility of shelling out this money for a publication seems marginal and pying this money for publication fees seems almost frivolous. In the US, funding agencies require, at most, that publications resulting from their funding would, be available on Pubmed Central within a certain time period and many non-OA publications comply, or they would lose the ability to publish a large chunk of NIH/NSF funded research projects. But doing so is not really timely OA. The bottom line is, if the grant is smaller than R01 size, many applicants would rather budget the expected $8000 of OA fees for the 3-4 year grant period for other line items that have a more palpable payoff, so to speak.
I don’t really have a point to this post, other than raising a problem that seems to be ignored, or marginalized, by many OA advocates. Not everyone operates on large grants. Many lab budgets leave very little room to buy a new laptop, let alone pay for an OA publication (typically the price of two of said laptops).
Glimmer is a program that predicts ORFs in bacterial and archeal genomes. The input is the assembled genome FASTA file, the output are several files of the predictions in different stages. The terminal output file is the .predict file. which looks something like this:
>NODE_1_length_38001_cov_935.551880
orf00001 481 362 -2 1.45
orf00002 451 567 +1 0.59
orf00004 3691 623 -2 5.43
orf00006 4254 5228 +3 4.65
orf00007 5204 5326 +2 7.04
orf00009 5587 6921 +1 5.20
orf00011 7062 8135 +3 5.48
orf00013 8327 9238 +2 4.26
orf00014 9241 10116 +1 3.26
orf00015 10119 10280 +3 2.81
orf00016 10296 10673 +3 6.61
orf00017 11288 10683 -3 6.35
orf00018 11910 11305 -1 7.18
orf00019 12313 11894 -2 5.22
The first column is the predicted ORF ID, the second is the start position, the third is end position, the fourth is the reading frame used and the fifth is a reliability score. For full details and how to install glimmer see Glimmer’s documentation. Glimmer3 also comes packaged with Ubuntu.
Here is a short Python script whose input is the genomic FASTA file which contains a single, assembled sequence and the glimmer file. The output is a FASTA file containing a separate entry for each predicted gene. Of course, it uses Biopython.
#!/usr/bin/env python
import sys
from Bio.SeqRecord import SeqRecord
from Bio import SeqIO
glimmer_file = sys.argv[1]
fasta_file = sys.argv[2]
# Read the sequence file
seq_record = SeqIO.parse(open(fasta_file),"fasta").next()
outseqrecords = []
# Read the glimmer file, record by record
for inline in file(glimmer_file):
if '>' in inline:
seqname = inline.split()[0][1:]
outfilename = "%s_g3.tfa" % (seqname)
continue
if "orf" not in inline:
continue
orfname, sbegin, send, rf, score = inline.strip().split()
sbegin = int(sbegin)
send = int(send)
rf = int(rf)
# reverse complement
if rf < 0:
sbegin, send = send, sbegin
sbegin -= 1 # Python indexes start a 0
score = float(score)
# split the sequence record
newseq = seq_record.seq[sbegin:send]
if rf < 0:
newseq = newseq.reverse_complement()
# Add a sequence record to the output
seqrecord_description = "begin=%d end=%d rf=%d score=%.2f" % (sbegin+1, send, rf, score)
outseqrecords.append(SeqRecord(newseq,id=seqname+"_"+orfname, description=seqrecord_description))
SeqIO.write(outseqrecords,open(outfilename,"w"),"fasta")
To run simply type:
./split_by_g3.py predict_file_name fasta_file_name
The output will be named with a _g3.tfa suffix. You can change that in line 17 (variable “outfilename”).
Respected Sir,
I am Distinguished Professor First Class Nebulous Nimbus, Department of Organismal Motility of the University Technicality of Upper Freedonia. I have many articles accepted and pending in PLoS Biology, PNAS, and BMC. Unfortunately I cannot pay the Open Access publication costs as my University has suffered abysmally from ill-advised investments in derivatives both partial and directional applied by the Math & Freakonomics department. A plaque on both their houses.
Sir, your reputation as a reverent and eminent scientist proceeds you. I have carefully sifted you for to assist Freedonian science from bottomless finance pit. I would be graciously to add you as honorific author in good position and standing to my articles, if you would be so kind as to send me Western Union the publication money needed by these journals in most urgent immediacy.
Please contact me in highest importunate on this matter: nebnim@ufd.ac.fd
Sincerely,
Docent Professor Doktor Nebulous Nimbus
(Celebrate #oaw09 Open Access Week)
One aspect of living in any kind of social setting is being assessed, rated and tested by one’s peers. Constantly. We are social creatures: we need to know who we are up against in any given setting. It is, after all, a matter of life and death, or at the very of gene dispersal. We have replaced butt-sniffing, teeth baring and chest drumming with “..the firm handshake / A certain look in the eye, and an easy smile” for first impressions. (Although I would personally take butt-sniffing over certain club ties most days.)
But we do not only look for first impressions. We look for long-lasting impressions, we want to see the future. Our future of course, but also the future of our kith and kin. After all, our kin carry some of the genes we are imbued to disperse: we would like to take care of that. But also our kith, our extended tribe members, current, future and pending: if we take this wolf to the pack will it be able to hunt as well as the rest of us? Will it slow down the pack during migrations? Will it dominate the herd in a year? Will it steal all our females and eat all of our cubs? Will it not pull its weight during hunting expeditions?
Credit: WickedSunshine.com
Welcome to the loopy and lupine world of metrics.
The wolfpacks of academia (read: departments) have a whole culture of ranking and assessments. Before the tenure-track wolf is accepted, a long list of future metrics are being brought out: in which packs did he PhD and postdoc? What do the pack leaders say about him? (reference letters) How good are his hunting skills (papers, conferences, invited talks) How good are his social skills? (Interview, more reference letters, phone calls).
After Dr. Wolf is finally accepted in the pack (from about 150 howling to get in), the hunting and fighting skills are put to careful periodic testing: how many grants? How much money? From which agencies? How many conference talks? How many invited talks? How are the teaching evaluations? And of course: how is the research? How many papers? Where? What is the impact factor of the journals in which Dr. Wolf publishes? In some (I would like to think more enlightened) packs, other article-level metrics are being used. At the same time there are, of course, the personal metrics: What is Wolf’s h-index? g-index? h-b index?
Dear wolves, cubs and assorted members of Kingdom Animalia: what is your favorite Canis lupus related metric if at all? Poll on the right, you know the drill.
As resident bioinformatician in many places over the years, I got many of requests to help. Anything from a short blast run to a full-fledged collaboration. I love that. I always like learning about new problems, and those requests may blossom into full research collaborations. So yes, drop me an email or step into my office any old time. But here are some sure-fire ways to tick me off:
- Send me sequence data in a MS-Word, PDF or pretty much anything else that is not a text file. No, PowerPoint is not an acceptable file format either.
- Send me sequence data not in FASTA format. Unless there is a compelling reason, FASTA only please.
- Please compress big files before you email me. Or let me know in advance that they are big, we’ll get them across by FTP or somesuch.
- Send me image files of protein structure prediction from some online server with the tag “what do you think”? How should I know what to think?About what? Nice colors man, try using green for your beta strands the next time, brings out your eyes. Also, if you want to perform structure prediction, approach it just like any other experiment. Take time to think what you are doing. Or come to me if you are not sure before you do a 3 day run.
- Say “78% homology”.. OK, but I wrote about that before. More than once.
- “Can you please BLAST this sequence for me and tell me what you think”? Huh? What is this? Why this particular sequence? How did you come by it? Why do you want to BLAST it? What is your scientific question?
- Actually, the above is probably the most common problem. No question on hand. Usually, when I manage to pry the question out of you, we find out that BLAST against the nr database with default values might not be exactly what the doctor ordered. (At least not Dr. Friedberg).
- “I really need to get some nice blast/tree/multiple sequence alignments for this grant application I am writing”. Always happy to help, but not 48hrs before the submission deadline. I have my own research and a life too, such as it is.
- No follow-up: OK, my lab did some work for you, anything between a couple of days and a couple of months. Now what? Can you give a sign of life letting me know if anything came out of it? Most hypotheses go down the drain, sure. Or sometimes funding runs out, things get prioritized differently, a postdoc leaves… but let me know! I worked quite a bit on this problem, I think that I deserve to know what happened with my work. Have some common courtesy.
Following the post on methane release in the Arctic due to global climate change, here is an informative image comparing temperature differences between two five year periods: 1999-2003 and 2004-2008. The time window comparison shows a significant warming in the arctic,when compared to the rest of the planet. Created by the people at The Real Climate based on data from the National Centers for Environmental Prediction.
The animated figure shows the temperature difference between the two 5-year periods 1999-2003 and 2004-2008.
Blog Action Day focuses this year on climate change, which, like everything else on this planet, is also a microbial matter. Howzat? Methane (CH4) is a greenhouse gas which has heat retention capability 23 times of that of CO2. Soil methanogens are the chief global producers of methane. There are an estimated 7.5x 109 tons of methane trapped in a frozen peat bog in West Siberia which constitute 25% of the estimated methane trapped in soil and ice-age permafrost worldwide. Due to global warming, this permafrost is melting, releasing methane, which in turn contributes to global warming in a vicious cycle. The Nature paper, and an article in TerraNature.
Not only there, but trapped methane in the melting Arctic Ocean is also being released. The ocean floor permafrost is melting, clouds of gas bubbles are welling up in “methane chimneys”
These “methane chimneys” sometimes contained concentrations of the gas 100 times higher than background levels and were so large that clouds of gas bubbles were detected “rising up through the water column,” Orjan Gustafsson of the Department of Applied Environmental Science at Stockholm University and the co-leader of the expedition, said in an interview. There was no doubt, he said, that the methane was coming from sub-sea permafrost, indicating that the sea bottom might be melting and freeing up this potent greenhouse gas.
Susan Q. Stranahan, environment360
This may be the only permafrost we will have in a few years
The concern is that methane release might lead to a tipping point in global climate change: flipping a switch rather than turning a dial. At some point, global warming might turn into a runaway scenario when a critical concentration of atmospheric methane is reached. Martin Kennedy and colleagues at the University of California, Riverside claim that this is how Snowball Earth has ended 635 million years ago: a rapid warming period following a runaway positive feedback prompted by a methane pulse.
The effects of permafrost thaw in Dawson City, Canada
How big a problem is this? Big. We have only recently begun to understand the magnitude of the role of methanogens in soil chemistry. It is very large. Even in arctic climes, cold adapted methanogens are active at below 0C temperature, down to -20C. However, a study conducted by Dirk Wagner and colleagues shows that a 3 degree rise in soil temperature from -6C to -3C would increase methane production dramatically. This means that not only trapped methane will be released due to soil thawing, but also that methane production itself will increase due to more favorable growth conditions for soil methanogens. So permafrost thawing hits the atmosphere with a double-whammy of methane release, supporting the concern about a runaway positive-feedback cycle that will cause sudden climate change.
Dr. Permafrost may actually be the hero here, rather than the villain
Walter, K., Zimov, S., Chanton, J., Verbyla, D., & Chapin, F. (2006). Methane bubbling from Siberian thaw lakes as a positive feedback to climate warming Nature, 443 (7107), 71-75 DOI: 10.1038/nature05040
Kennedy, M., Mrofka, D., & von der Borch, C. (2008). Snowball Earth termination by destabilization of equatorial permafrost methane clathrate Nature, 453 (7195), 642-645 DOI: 10.1038/nature06961
WAGNER, D., GATTINGER, A., EMBACHER, A., PFEIFFER, E., SCHLOTER, M., & LIPSKI, A. (2007). Methanogenic activity and biomass in Holocene permafrost deposits of the Lena Delta, Siberian Arctic and its implication for the global methane budget Global Change Biology, 13 (5), 1089-1099 DOI: 10.1111/j.1365-2486.2007.01331.x
Seven shades of brilliant, put a big smile on my face.
I am preparing a class on the origins of life for next week. The textbook I am using does not go into the Replicators First vs. Metabolism First argument, but I probably will, if I have time. Below, a quick refresher for those who know of the competing theories, and an unsatisfying introduction for those who don’t. In the end, you will ask to weigh the evidence and vote. Remember: your vote is important. I had a lousy week and seeing some numbers on the sidebar would be a nice ego-boost. Yes, that lousy.
From James W. Brown's lab, NC State University http://www.mbio.ncsu.edu/JWB/soup.html
Replicators First
Aka RNA World: RNA emerges as the first molecule that can replicate and perform enzymatic processes. It stores information and it is biochemically active. Thus it can both replicate and control a primitive meabolism. Later came the transition to DNA as an information storage, and the enzymatic role was mostly relegated to proteins.The first replicators might not even have been RNA molecules, but some pre-RNA nucleic acid such as PNA or TNA.
This theory is supported by the present-day existence of ribozymes, RNA enzymes. Especially the ribozymic activity in the ribosome, the platform of protein translation. RNA can also catalyze its own replication, up to a certain length (189 bases was the longest self-replicating RNA synthesized in a lab). Finally, RNA can also catalyze the formation of peptide bonds between amino-acids, setting the stage for the transition to an RNA+protein world. At some point, these reactions were cellularized by liposomes or other protobionts (pre-cellular structures with a protein, fatty or water boundary).
The arguments against the RNA World / Replicators First hypothesis are that RNA is labile, especially in water. Hence, an RNA world may not have been sustainable to become complex enough to recruit protein and bootstrap itself to the next level. Also, RNA is too complex to have been any kind of first player, and there were probably many chemical selective events prior to the appearance of RNA, as argued by the Metabolism First proponents.
Metabolism First
Metabolism First holds that metabolic processes assembled prior to the existence of replicators. Günter Wächtershäuser proposed that the pioneer organism originated in high (>100C) temperatures in hydrothermal vents. This organism resembled the catalytic converter in a car, more than a primitive cell: it had a composite structure of a mineral base with catalytic transition metal centers, such as iron-sulfide and nickel-sulfide. Dissolved volcanic gases would flow over this natural catalytic converter, yielding more complex compounds. Some of those more complex compounds would stick around, and incrementally form more complex molecules, eventually capable of catalysis. Once strong experimental evidence in favor of Metabolism First is the ability to recreate most of the Citric Acid cycle — both universal and essential in all life — without enzymes, and in high temperature and pressure conditions, such as those existing in underwater volcanic vents, favored for being the crucible of life.
Information bearing molecules like nucleic acids, came last, rather than first. Metabolism First explains the chemical evolution of catalytic versatility before the appearance of complex polymers. Also, the argument made by Metabolism First proponents is that RNA itself is a precondition, but a molecule too complex to have arisen by initial chemical selection. Metabolism First offers the necessary chemical scaffolding enabling replicators to appear on the stage.
Replicators (genetics) First vs. Metabolism First. Barbara Aulicino and Morgan Ryan
There is a lot more to the two hypotheses, of course. Including experimental evidence supporting both. Here are two reviews. Read them, and don’t forget to cast your vote here → →
In support of Metabolism First:
Trefil, J., Morowitz, H., & Smith, E. (2009). The Origin of Life American Scientist, 97 (3) DOI: 10.1511/2009.78.206
In support of the RNA World (Replicators first):
Müller, U. (2006). Re-creating an RNA world Cellular and Molecular Life Sciences, 63 (11), 1278-1293 DOI: 10.1007/s00018-006-6047-1
All the roots hang down
Swing from town to town
They are marching around
Down under your boots
All the trucks unload
Beyond the gopher holes
There’s a world going on
Underground— Tom Waits, “Underground”
Our picture of the microbial biosphere is heavily skewed towards what we can see, culture, and are interested in. E. coli is the most touted example: a relatively rare and ineffective bug in our gut became the all-time favorite model organism, because it grows so well in a petri dish, and is easy to manipulate. Our interest in human, animal and crop pathogens is higher than our interest in bacteria that are invovled “only” in nutrient recycling, and both are studied more than those that serve no apparent function, good or bad, in our anthropocentric world.
“One percent!” is the mantra by professors teaching Microbiology 201 “we only know of 1% of the species out there!” Also, there is a whole world of bacteria that we cannot perceive: they are too rare to come up in a microscope sample, and they are too finicky to grow on E. coli‘s favorite food, LB jello. Many are physically unreachable: they live kilometers underground, or on the ocean’s bottom, in the stratosphere, or in an insurance company’s checkbook. Many are well-hidden in plain sight: there are thousands of species in a handful of soil or a bucket of seawater: how can we possibly expect to typify them all?
The idea that there is a world of life that is hitherto unknown has always seduced us. The Kraken, Mermaids, Loch-Ness monster, Bigfoot, Yeti. We are in love with the concept of life’s rarities. Very rarely, a new mammal, reptile, bird or fish is reported. That usually goes unnoticed by the mass media, unless they are unusual, (as in butt-ugly). As a rule, we are not excited by the discovery of yet another species of fish (although we should!) but more by the “freak-appeal” of that fish. It excites us that it is strange, unusual, completely different than anything we know. It makes us happy to know that sometimes life cannot be pigeonholed. Biophilia proponents might attribute this to our inherent fascination with the diversity of life, and the role it plays in our own well-being. Remember the sealed ecosystem found in Ramla, Israel three years ago? There is the appeal of the unknown: “There’s a world going on underground”. Literally, in the Ayalon Cave. Also, the rare biosphere has an evolutionary appeal: it is the crucible of genetic novelty, where new gene variants spring eternal, and through lateral gene transfer fix themselves in the non-rare microbial communities. Unlike the isolated ecosystem of the Ayalon Cave, the rare biosphere is down under our boots, and constantly feeding the “common biosphere’s” gene pool.
New albino crustacean species from Ayalon Cave
For all these reasons, when metagenomics projects started getting off the ground, there was a lot of talk and excitement about the “rare biospehere”. Finally! Finally there is a tool with which we can gather those rare microbes and study them, at least on a genomic level. We will find the bacterial albino scorpions, an archaeal Coelacanth, and maybe even a viral Sasquatch or two. We don’t have to culture them (although we’d like to), we just need enough DNA and good computational tools to help us discover the weird genes of microbial life. And we will play Tom Waits on our MP3 players while we work at it.
Initial results were amazing: there is a world going on underground. And in the ocean, an acid mine drainage, in our guts, and on our skin. Using sequences of 16S rRNA, the “barcode of bacterial life”, to estimate the number of microbial species in a sample, a slew of new species was found. Ocean samples yielded thousands of proposed new species.
However, A recent study by Quince and colleagues published in Nature Methods tells us it may be time for a reality check. The problem being that even few reads may contain multiple errors, each one leading to unique sequences, interpreted to be new species from the rare biosphere. To correct these errors, Quince and colleagues look at the flowgram: the light intensities generated by the sequencing reaction. They used their own resequenced dataset to build an error model and better assess the diversity. Their results show that sequencing errors lead to species richness estimates that are two orders of magnitude too high.
What does this mean for the rare biosphere? It is probably out there, but the distribution tail could be much shorter than we think. Another thing is: how much influence would rare species have on the combined genomes of a microbial community or ecosystem? We don’t even know that yet. The best studies we have so far in that respect are of lateral gene transfer of antibiotic resistance, and fixation of new viral strains, especially influenza. But even here, we don’t know yet how rare is “rare”.
There’s a world going on underground. We are just beginning to bumble through it though.
Quince, C., Lanzén, A., Curtis, T., Davenport, R., Hall, N., Head, I., Read, L., & Sloan, W. (2009). Accurate determination of microbial diversity from 454 pyrosequencing data Nature Methods, 6 (9), 639-641 DOI: 10.1038/nmeth.1361
Sogin, M., Morrison, H., Huber, J., Welch, D., Huse, S., Neal, P., Arrieta, J., & Herndl, G. (2006). Microbial diversity in the deep sea and the underexplored “rare biosphere” Proceedings of the National Academy of Sciences, 103 (32), 12115-12120 DOI: 10.1073/pnas.0605127103
This has been a topic of discussion since I was in grad school: when will the Nobel prize for the structure of the ribosome be finally awarded? Well, it finally has. Ada Yonath, Thomas Steitz and Venkatraman Ramakrishnan received the Nobel for work that has spanned three decades and an equal number of continents.
First, a victory dance:
Next, the scientific background:
And part of Ada Yonath’s model in this clip:
