According to of all people Nick Matzke this is overreaching. Now Nick
had originally found a whole bunch of homologies showing that it
wasn't likely to be irreducibly complex. Here's the table of
homologies found with less than 1 e05 statistical significance.
http://www.nature.com/nrmicro/journal/v4/n10/fig_tab/nrmicro1493_T1.html
This left the number of indispensable, unique (non homologous)
proteins at 5%.
Then this paper hits and Nick shreds it. Why? Because the homology
evidence was not there for a single pre-cursor gene. Of course, Behe
caught on to this and gave a hand-waving critique like this:
> Nor were they curious about other some pretty obvious points: 1)
> What kind of amazing protein would it take to actually be able to
> give rise to the disparate physical parts of the flagellum? 2) The
> authors of the paper find few homologies between flagellar proteins
> and other proteins; yet if that primordial protein were indeed so
> plastic, why hasn’t it been co-opted to perform many other
> functions in the bacterial cell? 3) In their scenario, the prodigy
> protein gave rise to all the core parts of the flagellum billions
> of years ago, before the common ancestor of major classes of
> bacteria. Yet since that time it has not been heard from. A single
> protein which blossoms to give one coherent, astoundingly complex
> structure and then, its work complete, is never heard from again —
> that hardly seems like what one should expect on Darwinian grounds.
Now compare and contrast with Matzke. Now this is how you critique a
paper.
As the discussion over the Liu-Ochman flagellum evolution paper
continues, it is clear that I need to do a little more arguing to
defend my position. Although some were convinced that skepticism was
justified based the previous PT posts (basically: 1. this goes
against much prior published knowledge and 2. just look at the
obviously different structures), others have defended the paper or at
least suggested that the alleged problems are not as overwhelmingly
obvious as they seem to me. Two primary lines of argument have been
raised. First, some have pointed out, correctly, that the reputation
of the authors and journal in question far outweighs the reputation
of a blogger like me, so why should readers trust me over PNAS? I
will concede the case when it comes to reputation; all I can say is
that over the years I have developed some familiarity with the
literature pertinent to flagellum evolution, and as I read through
the PNAS paper it became apparent that it was going against much of
what was already known. This is not necessarily bad if a direct
attempt is made to rebut conventional wisdom, but if assertions are
made without much evidence of awareness that they go against previous
work, that is problematic.
Second and more importantly, the Liu-Ochman paper reports reasonably
significant e-values (e < 0.0001) for their claimed homologies (all
of the lines in Liu-Ochman’s Figure 3 represent matches with e-values
of 0.0001 or less, in one or more of the 41 bacterial genomes they
searched). I have been hinting that there are more technical problems
with the paper, and that I and some others are working on a more
detailed critique. For the moment – especially to forestall
suggestions that we are ignoring Liu and Ochman’s BLAST results, and
that we don’t know how BLAST statistics work, etc., I will post some
preliminary results of an attempt to replicate Liu and Ochman’s
findings.
A little background on BLAST, e-values, and homology
BLAST stands for Basic Local Alignment Search Tool, a standard
program in bioinformatics that is used to find statistically
significant matches between two sequences (amino acid or DNA). It is
implemented in numerous web applications that can search massive
online databases, and in stand-alone executables that can search
local or online databases.
Homology is similarity due to common ancestry. In proteins and DNA,
this is typically sequence similarity. As a very rough guide, for
protein amino acid sequences, sequence similarity of 30% or more is
typically strong evidence of homology, sequence similarity of 20-30%
is the “twilight zone” where the assignment of homology typically
becomes uncertain, and sequence similarity below 20% can often be due
to chance resemblance. (Various details make this picture more
complicated, e.g. shorter proteins need higher similarity to
confidently assign homology.)
Structure is more conserved than sequence. It has been repeatedly
observed that proteins down to 30% or 20% similarity will commonly
exhibit very similar tertiary structure and folds. There are ways for
mutations to change structures so this is not a universal rule, but
it is a very good generalization. Homology will often be assigned
based on detailed structural similarity and weak sequence similarity.
It is thus suspicious if a claim of significant sequence similarity
is contradicted by the observation of no structural similarity.
Along with alignments, BLAST produces an e-value statistic, which is
a better statistical measure of the significance of an alignment than
percent similarity. The e-value represents the number of times that a
given sequence match of a certain length and strength would be
expected by chance, given a database of a certain size. (“e” is for
“expected”) The larger the database, the more likely it is that a
weak match would occur by chance. An e-value of 1 indicates that one
match of similar length and strength or better would be expected by
chance, and therefore the match is clearly not significant. There is
no hard and fast line for significance, and the e-value is not an
infallible statistic anyway, but the rules of thumb seem to be that e-
values less than 0.01 are interesting, and e-values less than perhaps
10-8 or so are almost always a good indicator of homology, assuming
no human error. Very close matches – 50% or more sequence in common –
can have e-values of 10-30 or less. Identical proteins, e.g. a
protein BLASTed against itself, will have an e-value of 0.
An attempt to replicate the homology hits in Liu and Ochman (2007)
Recall Liu & Ochman’s Figure 3:
The lines represent alignments that are significant according to an e-
value cutoff of e = 0.0001 or less. The numbers represent the number
of genomes (out of 41) where the homology connection was reported.
The blue lines represent the matches found specifically in the E.
coli K12 genome. According to Figure 3, FliC is homologous to FliD
(cap protein), FlgD (rod), FlgE (rod), FlgK (adapter between hook and
FlgL), and FlgL (adapter between FlgK and FliC). Homology between
FliC and FlgL seems to be well-accepted and retrievable with PSI-
BLAST (a search more sensitive than regular protein BLAST), but the
others are novel, or at least it is novel to claim that a simple
BLAST search can detect them with decent significance.
I and others have been attempting to replicate the results in Figure
3. According to the paper’s methods, Figure 3 is based on pairwise
comparison using the executable bl2seq (BLAST 2 sequences). The
bl2seq executable can be downloaded from the NCBI here. (I got
blast-2.2.10-ia32-win32.exe to work on my 2004 windows32 PC; you will
have to download other versions for other machines and operating
systems.) The bl2seq documentation is online here. According to Table
1 of the paper’s Supplementary Material, the E. coli genome was E.
coli K12, NC_000913.2, which is online here. I downloaded the FASTA-
format sequences for the 24 “core” flagellar proteins that the
authors identified; I have uploaded them here (right-click to
download) as a zipfile if you would like them.
The table below shows the search results for BLASTing FliC against
the 24 flagellar proteins. The table columns, from left to right,
contain:
* 1. Protein name
* 2. Liu-Ochman matches to FliC (from Figure 3)
* 3. e-values for bl2seq search default filters off
(example search: bl2seq -p blastp -F F -i FliC.fasta
-j FlgD.fasta -o FliCvFlgD.out)
* 4. e-values for bl2seq search default filters on
(example search:bl2seq -p blastp -i FliC.fasta -j
FlgD.fasta -o FliCvFlgD_filters.out)
* 5. e-values for bl2seq, default filters on, database size = 7163
(example search: bl2seq -p blastp -i FliC.fasta -j
FlgD.fasta -o FliCvFlgD_filters_db7163.out -d 7163)
* 6. e-values for bl2seq, default filters on, database size =
293683
(example search: bl2seq -p blastp -i FliC.fasta -j
FlgD.fasta -o FliCvFlgD_filters_db293683.out -d 293683)
Although the methods section of Liu & Ochman (2007) says that the
bl2seq BLAST searches were run with defaults (basically column #4 in
the table below), it is apparent that the BLAST searches were
actually run in the non-default setting of filters off (column #3).
Through the grapevine I have heard that the authors are telling
correspondents about this error in email, and plan to issue a
correction, which is good.
An additional issue is database size. Searching 23 proteins instead
of one means that the database size is not the size of one protein,
but the size of all 23 proteins strung together, or 7163 amino acids
in length. Furthermore, the authors actually ran these pairwise
searches between the 24 core proteins in each of 41 genomes, so the
full size of the database searched is actually approximately 7163 x
41 = 293683. Columns 5 and 6 show the resultant e-values when bl2seq
is run with the -d (database size) parameter set at these values.
Table: e-values resulting from bl2seq search of E. coli K12 FliC
against 23 other core flagellar proteins, using different search
options. ns = no significant hit according to Figure 3 of Liu and
Ochman (2007). na = no significant alignment returned by bl2seq.
Protein Liu-Ochman hits for E. coli K12 (Figure 3) default filters
off default filters on default filters on, database size = 7163
default filters on, database size = 293683
FlgB ns 0.2500 0.2500 na na
FlgC ns 0.3200 2.1000 na na
FlgD <0.0001 0.0003 0.0110 2.3000 na
FlgE <0.0001 4e-06 0.0110 0.2100 na
FlgF ns 0.0120 0.0120 0.3500 na
FlgG ns 0.1700 0.6600 na na
FlgK <0.0001 2e-10 3e-05 0.0100 na
FlgL <0.0001 4e-09 0.0250 na na
FlhA ns na na na na
FlhB ns na na na na
FliD <0.0001 9e-09 7e-06 0.0080 8.0000
FliE ns na na na na
FliF ns 0.0350 0.0350 0.4600 na
FliG ns 0.8600 0.8600 na na
FliH ns 1.7000 1.7000 na na
FliI ns 1.2000 1.6000 na na
FliM ns na na na na
FliN ns 0.2500 0.2500 na na
FliP ns 5.2000 5.2000 na na
FliQ ns na na na na
FliR ns na na na na
MotA ns na na na na
MotB ns 0.6100 0.6100 na na
As you can see, with default filters turned on, 5 significant hits
become only 2. With filters on, plus database sizes larger than a
single protein, no hits are significant.
Removing filters from a BLAST search is an extremely serious decision
with major impacts on an analysis, because the filters prevent
spurious matches that are due to similarities that are not
phylogenetically informative, such as low-complexity regions and
biases in amino acid composition. Similarly, the database size has a
massive impact on e-value.
We have not yet run the same searches systematically through the
other flagellar proteins and the other 40 genomes, but it is apparent
that the results would be similarly dire, and that most or all of the
new significant hits reported in Liu and Ochman’s Figure 3 would
evaporate. Thus the only support for the all-flagellum-genes-from-one
hypothesis, which was unlikely from the beginning based on background
information, also evaporates.
Acknowledgements
Doug Theobald and Ian Musgrave ran some of these searches before me,
and Doug educated me on the database size issue and made various
other helpful comments. Any errors are of course mine.
Note: The FliC-FlgD match in Column 3 has an e-value of 0.0003, which
is actually higher than the 0.0001 cutoff. So either there is a
slight difference in our databases or techniques, or 0.0003 was
mistakenly reported as a hit below 0.0001 in Figure 3.
On Apr 24, 2007, at 12:39 PM, David Campbell wrote:
> Biological complexity reduced:
>
> Open access to the full article at
> http://www.pnas.org/cgi/reprint/104/17/7116
>
> Stepwise formation of the bacterial flagellar system
> Renyi Liu* and Howard Ochman*†‡
>
> Elucidating the origins of complex biological structures has been
> one of the major challenges of evolutionary studies. The bacterial
> flagellum is a primary example of a complex apparatus whose
> origins and evolutionary history have proven difficult to reconstruct.
> The gene clusters encoding the components of the flagellum
> can include >50 genes, but these clusters vary greatly in their
> numbers and contents among bacterial phyla. To investigate how
> this diversity arose, we identified all homologs of all flagellar
> proteins encoded in the complete genome sequences of 41 flagellated
> species from 11 bacterial phyla. Based on the phylogenetic
> occurrence and histories of each of these proteins, we could
> distinguish an ancient core set of 24 structural genes that were
> present in the common ancestor to all Bacteria. Within a genome,
> many of these core genes show sequence similarity only to other
> flagellar core genes, indicating that they were derived from one
> another, and the relationships among these genes suggest the
> probable order in which the structural components of the bacterial
> flagellum arose. These results show that core components of the
> bacterial flagellum originated through the successive duplication
> and modification of a few, or perhaps even a single, precursor
> gene.
>
>
>
> --
> Dr. David Campbell
> 425 Scientific Collections
> University of Alabama
> "I think of my happy condition, surrounded by acres of clams"
>
>
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Received on Tue Apr 24 23:50:44 2007
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