Among a couple of very interesting posts received as comments in Ciencia Hoje blog
(http://cienciahoje.uol.com.br/noticias/2013/04/polemica-a-mesa), I got a
message from Vinicius Vilperte arguing that risk assessors should use omics to
add valuable info to their risk assessments. Even more, he argues that Southern
and Northern blots and many other conventional molecular info supplied by the
applicants in risk assessment dossiers for the risk agencies are not very
informative.
I would like to argue against most molecular
data supplied in these dossiers. Indeed, in my opinion, which is coincident to
that of other risk assessment specialists, molecular data do not significantly
contribute to risk assessment. I had this feeling since long ago, but it was
very much reinforced during a Hesi meeting I attended in Paris two years ago
and the feeling was consolidated in the last year, when a couple of risk
assessment specialists wrote the first guide on GMO environment risk assessment
and I was among them. It was a long period of apprenticeship (which, in fact,
never ends), but I am confident my feeling was right and will try to explain
the main issues to the reader.
I kindly ask the reader to keep in mind that
the GM plant in the market is not a regular event produced by a researcher in
his lab: it is the result of a very stringent selection of thousands of
candidates that need to behave EXACTLY as expected. Hundreds of different
parameters are evaluated in dozens of different situations and the final ELITE
EVENT has all the expected phenotype and no unexpected characteristics.
Let us begin with a sensible list of molecular
data that can be generated by a reasonable cost
a) Sequence
of the insert
b) Sequence
of the flanking regions
c) Chromosome
localization of the inserts by FISH
d) Chromosome
localization of the inserts by Southern blot
e)
Specific gene expression at the RNA level by Northern blot
f) Investigation
on other RNAs by Northern blot
g) Insert
stability by PCR analysis
h) Specific
gene expression at the protein level by ELISA and Southern blot
We can add to the list some more expensive
approaches.
a) Super
SAG tags or microarray data on gene expression
b) 2D
gels for protein profile studies
c) Maldi-TOF
and other proteomic approaches to generate detailed protein profiles
d) Metabolic
flow charts
e) Whole
genome sequencing
f) Large
scale direct RNA sequencing
The first list of data is frequently available
in dossiers and papers. What does it tell us that could be useful for risk
assessment?
·
The
exact insert sequence is important to evaluate the potential for the production
of truncated peptides and many other problems derived from breaking and
rejoining the genome and the insert. It is certainly informative.
·
Sequencing
the flanking region is important for PCR detection of the even, but does not
bring useful information for the risk assessor. Many colleagues may argument
that it is nice to precisely identify the insert location, since it may have
disrupted important genes and created spurious ORFS. Yes, that may happen, but
if such a thing happens and has detectable deleteriouys consequences, it will
most possible lead to unstable expression of the transgene, changes in the
regular protein/lipid/sugar profile, in the hundreds of agronomical parameters
evaluated, etc. Unanticipated changes do occur, but any event having unexpected
phenotypes is discarded in the selection process, as alerted above. There is
presently not a single report of this kind of information as important for any
specific risk assessment, from hundreds of elite events analyzed in commercial
release assessments.
·
Chromosome
location (by both methods) and copy number are nice information, but are really
not informative. The important information is insert sequence. If the event has
more than one such copy, it is important to know all of them, not that much
because of safety reasons, but to help on the interpretation of all
experimental results using molecular biology. Similarly to what I said before,
there is not a single evidence that copy number is important for biosafety and
risk assessment.
·
Gene
expression at the RNA level may be useful as a tool to understand how the
transgenic plant deals with the product and its eventual translation. But, like
chromosome location, it is not important for risk assessment: a lot of
intervening issues cross the road to protein expression, which is the important
quest in most cases. Obviously, specific RNA expression may be important in
iRNA-based GM plants, but even so we can´t generalize its usefulness as a bare
info for risk assessment.
·
The
use of blots to detect other RNAs than the target ones is disputable, but if
the researcher has the precise sequence of the insert, there is no reason to
believe that unanticipated RNAs will be a source of problem. They may even
exist, but if they do not change the many hundred parameters that are measured
to ensure that the event elite is behaving as expected, they are not important for the risk assessor.
·
Insert
stability (its presence and expression) is obviously very important for the
technology, but is absolutely useless for risk assessment. Data usually support
the plea that the GMO is doing what is expected from it. What if the insert is
eliminated? The GMO will not work properly, but it will not be more “dangerous”
than the original event.
·
Specific
gene expression is a very important issue, especially if data is produced to
demonstrate expression levels of the relevant proteins in different tissues, in
different conditions. This is something that has consequences on an eventual deleterious
impact of a GMO. However, not every protein should be investigated: data must
be generated on the proteins encoded by the transgenes. A plant has hundreds of
thousands of different proteins and it is nonsense to try to investigate
changes in all of them. If any subset is selected, due to technical
limitations, it is a bias that completely invalidates claims of “broad”
expression analysis for risk assessment use: the discarded subsets can well
have the “relevant” proteins.
Summarizing the analysis of the first block of
techniques, both insert sequence and protein expression levels (sometimes RNA
expression levels also) are important molecular data. Everything else is nice
to know, but has very limited use in risk assessment.
Let us now discuss the omics block.
·
Transcriptomics,
either using SAGE, microarrays or other techniques, can produce an incredible
amount of data in very short time and the costs are sinking fast. However
fancy, this data is of very limited use for risk assessment and the main reason
is: we don’t have large enough baseline data as to be able to come to any
significant conclusion related to risks. Moreover, even if consistent changes
of a certain group of genes are observed between transgenic and non-transgenic
plants, it may and possibly will have no meaning for risk assessment unless a
route to damage could be established for any of these genes and gene products
or for the metabolic change, as a whole. This is a daunting challenge and we
are VERY FAR FROM BEEN ABLE to use the info.
·
The
same can be said of proteomic data, with the aggravating circumstance that
proteomics is still unable to produce data from a large set of proteins. As
said above, if a selection is made, we may be missing the “dangerous” protein,
left in the other subsets. Moreover, as for transcriptomics, a very limited
info is available for crop baselines. And again, as for transcriptomics, even
if we have some reproducible changes in protein profiles, it is very difficult,
if not at all impossible, to correlate them with risk issues. The use of
proteomics for risk assessment is an even larger challenge that the use of transcriptomics.
I will come back to this point when I discuss Zolla´s results, below.
·
Some
people claim we should re-sequence the whole GMO genome and try to find any
unexpected changes. The reader will be surprised to know that cells from a same
organism can have slightly different genomes! It would be very difficult to
know what was caused by the transformation and other post-transformation events
and what was natural.
To conclude, none of these new omics have
presently a clear use in risk assessment, even if their results are fancy and
informative for researchers in different areas.
The reader could well ask: if molecular data
are of little use, what are the main elements for a sound risk assessment?
As stated above, risk assessment is not a mysterious
process: it is a structured process in five steps, starting with the definition
of the context and ending up with a decision on risk acceptability. All the
molecular data discussed above is just a small part of the context. Many other
information is also important, maybe much more important: all phenotypic data
are of paramount importance and most of it is not of molecular nature. The biology
of the parental organism is also critical. It is therefore wrong to try to
impose molecular approaches as vital, indisputable and essential for a good
risk assessment. The formal proof that the present approach to a good risk
assessment is valid is the fact that not a single reliable report is available on
health or environment harms due to commercially released GMOs.
The figure below represents the five steps on risk
assessment.
Figure 1: Five steps on
risk assessment. The reader should have a close look on the five ellipses over
the first step. Moreover, he should bear in mind that for the estimation of
both probability of occurrence and magnitude of the associated harm a pathway
to the harm must be established for every danger imagined. Following this
approach it is possible to discard most imaginary dangers and concentrate on
the relevant issues.
To conclude this discussion, I would like to
emphasize that much information derived from lab experiments serves as warnings
to the risk assessor, but by no means should be regarded as essential.
Epigenetic modifications in gene expressions are a well know and much discussed
phenomenon and it may change gene expression in GMOs, but the stringent
selection of the elite event effectively eliminates any important change due to
it. This is also true for most other gene modulation phenomena. One should
never forget we are not dealing with lab experiments, but with long studied
elite events.
I hope to have been clear enough as to be of
some use to the reader, as an answer to Vinicius, who I suppose is now in the
beautiful but cold Norway.
Paulo Andrade
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