Monday, October 31, 2011

Gene expresion adaptation 'signs' in!

The review by Hunter Fraser discusses the role of gene expression in adaptation, the challenges facing the field, recent genome-wide studies that allow the rejection of the null model of neutrality and how the latter thus help to determine, with some confidence, if positive selection is occurring. He then goes on to discuss questions that can be addressed and the empirical evidence available for answering these.

Challenges in studying gene expression adaptation:
The author discusses the two important stages at which adaptation can occur - the inherent sequences of proteins and the pattern and level of expression of these proteins. Protein sequence evolution and its role in adaptation have received a lot of attention from the scientific community and have been widely studied. The study of gene expression adaptation (GEA) on the other hand, has been very limited. There are three reasons for this aberration - the little significance attributed to GEA in adaptation as compared to protein sequences until recently (as recent as 2003!), difficulty to characterize gene regulation as compared to deducing DNA sequences, mainly because of its dynamic nature, and thus the unavailability of suitable methods for simple and effective study of GEA.

On these lines, the paper discussion started by addressing basic questions like the meaning of gene expression and the role it plays in adaptation. Regulatory regions such as promoters and enhancers control gene expression. Studying these regulatory regions is complicated by factors such as mode of action (cis or trans?), location of cis-regulators (how many nucleotides upstream of the gene?) and the absence of an easily detectable direct product in addition to the dynamic nature mentioned by the authors.

Genome wide studies – Vm and sign tests:
The most important problem in tests of selection are determining a neutral reference for comparison of all results, and availability of adequate data to satisfactorily dismiss the null model of neutrality. Genome scale studies can help in providing an unbiased repertoire of data for this purpose. There are two strategies currently used in genome-wide studies on GEA.

The mutation accumulation strategy compares the mutational variance (Vm) under no selective pressure and uses this as a neutral reference for expression divergence in the wild. The author dismisses this strategy for in-depth study of GEA as it is able to detect only the dominant mode of selection. The main difficulty with this method is identifying what fraction of mutations between species lead to evolution.

The other strategy is based on sign tests where a number of quantitative trait loci (QTL) are measured according to an increase (+) or decrease (-) in the trait value caused by a parental allele. A trait under positive selection would have an unequal number of + and – alleles. It is important to consider that adequate QTLs are not available for a single gene to reject the null model of hypothesis. Also this test can be affected by relaxed negative selection (RNS), that is the tendency of down-regulation due to occurrence of random mutations, which would be (wrongly) observed as positive selection in the other lineage. Polarization of the results by a lineage that is an out-group can help in reducing the bias due to RNS at least for up-regulating GEA.

It was further discussed that though sign tests are useful, they lack “power” for identifying the extent of selection. The sign test measures the directionality of the change but doesn’t quantitate the change in that direction. Also, in some cases like genes involved in a pathway, down-regulation of repressors and up-regulation of effectors would give the same phenotype, but would be counted with opposite signs.

A more robust method is described which allows using sign test on QTLs acting on entire gene sets. The robustness of this method derives from the huge number of eQTLs studied and the use of only cis-acting eQTLs where independent effect can be easily established. The use of a polarizing group further enhances the ability of this method to reject neutrality. This method was a bit difficult to follow for most people in the group. The occurrence of cis eQTLs with same sign is only considered for “selection” to make a conservative (and robust) estimate of GEA. However, this doesn’t imply that other modes of regulation might not be involved in GEA. Also, the distinction between cis-acting and trans-acting genes is blurred. Although a gene lying on a different chromosome is definitely trans, what distance cutoff can be applied for those on the same chromosome? The case becomes complex for bacteria where only one chromosome exists. Also, what would be the case if many trans-eQTLs with the same sign are acting on the same gene set?

Future questions:
The author discusses a number of future questions that need to be addressed in the field. Some supporting evidence is already available for answering some of these questions. However, everyone in the group thought that these were majorly open-ended questions and possibilities, and the little evidence that was available was inadequate to establish any answers. However, the preliminary data available was interesting and some of the questions were discussed in detail.

“How often is GEA tissue or condition-specific?”
GEA offers an amazing advantage in that selection is based on the level of functioning of the genotype and requires no change in the protein sequence. This leads to the question if GEA occurs across all tissues or in certain tissues, as well if certain conditions cause GEA. The genome-wide study for tissue specificity was thought to be inadequate in terms of number of tissues studied.

“Does GEA affect single mutations of large effect or many mutations of smaller effect”
Genome-wide studies show that mutations of smaller effect are generally involved in GEA. It was discussed that single mutations of large effect might be economical for causing adaptation. However, the probability of single mutations reverting would be harmful for the individual and hence many mutations of small effect would provide a more robust path for GEA.

“Does GEA affect particular types of traits or genes”
Genome-wide studies can only help in answering this question without any bias. This question is important because if GEA affects a gene involved in many pathways, it can have a widespread effect.

“Are most evolutionary adaptations due to GEA or protein-coding changes or both?”
It is interesting that a genome-wide study indicates GEA to be the major contributor to evolution. This is interesting in light of the earlier question “What fraction of gene expression change is adaptive?” because a very small fraction of gene expression change is adaptive, but this little change is responsible for most adaptations.

Almost all of the questions require broad and intensive genome-scale studies to satisfactorily establish any results. The ability to detect a change and the choice of genes severely affects the results of the experiment. Also, robust methods need to be developed that can satisfactorily answer the impendig questions. Overall, the paper does a good job of presenting the new genome-scale strategies used to study GEA and the questions that need to be addressed in the field, constantly stressing on the importance of genome-wide studies in each case.

My views on the paper:
In the past, I have done genome-scale studies on elucidating regulatory regions in the genome. The challenges in determining GEA are an extension of the challenges I faced in determining putative regulatory sites. For example, determining the cis-regulatory region, effects of epigenetic modifications, etc. The complexity in determining gene expression, and in addition adaptation due to gene expression is no doubt a daunting task. Nonetheless, such genome wide studies can help us to gain a lot of insight into the mechanisms underlying gene expression and evolution.

Fraser, H. (2011). Genome-wide approaches to the study of adaptive gene expression evolution BioEssays, 33 (6), 469-477 DOI: 10.1002/bies.201000094

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