Biggest Virus Yet Found, May Be Fourth Domain of Life?
news.nationalgeographic.com
news.nationalgeographic.com
Edit: added "classification schemes" after taxonomy and phylogency to clear up confusion
> So how do you classify life?
I believe that's the point.
The NCBI's taxonomy browser is good for exploring the various lineages, e.g. here is the data for humans: http://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode...
2) Alive.
3) Somewhere in between.
2) definitely life: animals, plants, fungi
3) self-replicating systems that stay far from equilibrium for a substantial time: hurricanes, fire, Benard cells, viruses
One of those things is not like the others - the abstracted information and constructed envelope of a virus seems qualitatively different to me, since that structure doesn't depend on external factors for its basic coherence. +1 for mentioning Benard cells, did not know of those.
When new information blasts away an existing categorization scheme, then a new one does need to be made.
It really is bullshit, but it is useful bullshit.
The tree of life model is almost useless for understanding single celled organisms which vastly outnumber there multicellular counterparts. It also creates a lot of confusion where people assume the model matches reality instead of poorly mapping to reality. Consider species is a surprisingly vague concept where A and B may be compatible and A and C are compatible but B and C are not. The point is it's based on inaccurate assumptions, if you understand how and why the model breaks down you can extract a lot of value from F=MA or any other such approximation.
the hydrogenase that I work with is part of a mobile element; it's found in our organism in a gene island, but also straight up in the genome of another organism that was isolated 5000 kilometers away, in a totally different ecotype. both of these guys are found in the ocean, but none of the other members in the broad family that it comes from are oceanic: they come from terrestrial volcanic mats.
So obviously species-wise discrete or fuzzy taxa do not work for me; but even protein phylogenetics can be muddied by things like random gene fusions and convergent evolution. I'm not sure that some of the trees that I generate are actually taxonomic, but may be the result of selective pressure against a highly conserved scaffold.
Of course, they're still useful, even if they're bullshit... Because I don't care about phylogenetics, I care about function. If the tree that I make is an unfaithful representation of the historical record, no big deal, as long as it faitfully clusters function.
And even though we've been able to build a great story and a great tree structure, those of us who have studied deep biology know that the tree structure is really muddied by some inconvenient phenomena; for example, using some tiny subset of genes to compute phylogeny generates a very different result from using all genes, or combining all the gene data with all the morphogenic data. Understanding the underlying phenomena which help explain the oddballs that don't fit into the Dogma is almost always useful, because it helps us go back and refine the Dogma.
It's really more of a graph of life when you include horizontal gene trasnfer; whether that graph is very much non-tree like is still an open question.
Here's an example: let's say you're trying to develop a new drug against a virus. You can isolate huge numbers of the virus from infected individuals and sequence the DNA very cheaply. Using selective pressure analysis you can look for regions of DNA that are being selected for over multiple generations of the virus. Why? Because these might be important for survival. If your drug can target those regions of the protein, the virus will have an uphill battle mutating and creating resistance to your drug.
Here's one more: You can use phylogenetic techniques on individual proteins to see how closely related they are. Why do this? Because if you're developing an antibiotic, you want to know that the bacterial version has a sufficiently large evolutionary distance from the human version. Now you can do this at scale for huge numbers of bacterial proteins and identify the ones with the biggest distance from their human orthologs. This allows you to identify bad antibiotic targets upfront before pouring all sorts of money into experiments (or at the very least, going into said experiments with your eyes wide open).
Edit:
Also, hi Mike!
Beautiful examples in theory, but blown out of the water in practice.
In the current age of bioinformatics, practical modern antivirals and antibacterials are not being identified, not at all[0].
As such, I find it awkward and outdated to claim that novel drug discovery is a great testament to phylogenetics and bioinformatics.
Can anyone offer a practical example of how bioinformatics has genuinely been (pref. statistically) useful in successful drug discovery? I would also be interested in evidence that existing antibioitics could have been discovered more effectively, using new methods ab initio.
[0] e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2095086/ "the success rate of discovery has gone down"
As someone who knows nothing of bioinformatics or their relation to drug discovery, I see nothing wrong with his post. Perhaps what he is saying is incorrect, but can someone explain why?
I will admit that this work was largely done 10 years ago, so it's certainly a bit outdated. I will also concede that the current state of affairs in antimicrobials research is dreadful.
>Can anyone offer a practical example of how bioinformatics has genuinely been (pref. statistically) useful in successful drug discovery?
I can offer no data that stands up to a rigorous statistical analysis that you seek as far as bioinformatics impact on drug discovery is concerned. This is the primary reason most of us with bioinformatics experience who used to work in the pharma industry don't work there anymore (and for a great many of us this was not by choice).
>As such, I find it awkward and outdated to claim that novel drug discovery is a great testament to phylogenetics and bioinformatics.
Where did I make this claim? I chose the examples I did (which are real and actual examples by the way) of the way one can use phylogenetics to point out to the parent poster that it's not all crusty old geezers arguing about whether two almost indistinguishable variants of mice are part of the same taxon (which is the view a great many people who have never worked in the field often have). I chose these examples because they are practical examples of phylogenetic techniques that are easy to explain in a forum such as this in a few paragraphs.
I can't speak to the viral one, as I was only peripherally involved in that work, but my very first job was taking bacterial sequences, making alignments, and looking at the phylogenetic trees to see whether or not they were too close to eukaryotes for comfort. This was the policy of the company where I worked: the antimicrobials team wanted to ensure both spectrum (that targets they were going after were actually present in medically important bugs) and specificity (that they weren't sloppy targets that you might accidentally hit a human enzyme with). There were a great many targets that I personally de-prioritized because they were no good purely from an informatics perspective.
I understood you were using the example of antibacterial drug discovery to defend traditional phylogenetic models (against the parent commenter). I wanted to illustrate that the lack of new results in these fields doesn't offer good evidence that traditional phylogenetic models are adequate for this specific purpose.
Sincere apologies if I went too far and incorrectly put words into your mouth. Sorry.
I also don't work in bioinformatics any more for similar reasons.
http://www.evolution-outreach.com/content/6/1/11
for a discussion of this issue and other issues about problems in biology education.
Very broad classifications tend to be useful, in that by definition they group similar organisms. Beyond that level of detail they're useless, yes, but beyond that level of detail you're already going into the level of expertise where you're expected to recognize their uselessness. Cell Biology PhDs may or may not realize it, but they're working on tyrosine kinases, not classifications.
The problem is when people are taught in an authoritarian manner, and then they run into places that method breaks down, and so throw the baby out with the bathwater. As you have done.
What do you mean by "modern classification schemes"? If anything, molecular phylogenetics helped clarify earlier attempts at classification which used common morphological features.
One might argue that viruses are not life because they are obligate intracellular parasites - but many species of bacteria are too. It used to be that size was the distinction: viruses as small, simple particles and cells much bigger, but large complex viruses also blow this argument out of the water.
The only thing left that really distinguishes the "life" of cells and the "maybe-not-maybe-life" of viruses is the presence of ribosomes to synthesis proteins. Maybe one day we'll find a virus-type replicator that also contains ribosomes - and what then?
Then there is also the issue of simulating life in silico - at which point does it stop becoming just a simulation and can be considered 'actually' alive?
Personally I think these questions are best debated in the context of philosophy and ethics rather than taken as something that the domain of science ought to provide conclusions to.
Mimivirus, another giant virus, has its own gene for an amino-acyl t-RNA synthase, an enzyme that loads an amino acid onto a transfer RNA to be used in making proteins. That's awfully close to a ribosome component.
That is a horrible use of units.
A single hydrogen atom has diameter of about 10e-10 meters.
A single nanometer is 10e-9 (i.e. about 10 hydrogens stacked)
Viruses are on order of 50-100 nanometers => ~500-1000 hydrogens across.
These viruses are 10e-6 meters, or 10,000 hydrogens across.
That's too many to imagine and hard to remember, so other useful dimensions I sometimes use are
diameter of DNA helix is about 2nm (20 hydrogens across)
the size of X chromosome, which is roughly 7um.
the size of a cells in our bodies vary around 10-100um.
so, the best way to get a sense of scale for this particular virus (imo) is to imagine it as 1/7th of size of an X chromosome. Or as 10x bigger than an average virus.
Is this as significant as it sounds? Did they just find life that may have origins different than anything else on earth?
As as analogy to computer viruses and other malware, the effect is very much like the polymorphic program code found in some of these, albeit not going as far as to derive new functionality as biological mutations can, through the combination of mutation and selection.
Now it could be that if any one of these genes shows up in any of the "known" lineages (as it might if a retro-virus carrying it injected it) that it causes the host to die before reproducing. It could be that they are pandemics waiting to happen, it could be that they are code for additional eyeballs.
The challenge is that we do not yet (as far as I can ascertain) have a way to looking at a gene and identifying all of the effects that gene has on a cell or an organism. What we have are organisms with genes, that we are 'debugging by printf' by essentially commenting them out and seeing what happens.
Once our knowledge base flips, and we understand genetics at a information/programmatic level, we would be able to evaluate these 2500 genes and see if there is anything useful here.
Are genes Turing Complete. ;-)
Then during evolution it eventually lost every other piece of own biochemistry, eventually becoming a virus.
I think the uniqueness of this virus' genes suggests that whichever direction it happened, it happened very early in the history of life.
An image without a scale it worthless..
Not so. If you read the article, it says each is about one micron—a thousandth of a millimeter—in length.