RNA-seq: Dynamic Changes in the Transcriptome of Fission Yeast

Source: Wilhelm et al (2008). Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453:1239-1243.

The authors of this paper employ massive RNA-seq strategies to extract transcriptome data from S. pombe in several conditions (i.e. proliferation, stress, meiosis and etc). First they mke the case for the sensitivity of RNA-seq in finding the expressed regions that do not show up in tiling array hybridizations. Their first observation is that a major proportion of the genome, although to various extents, is expressed. They have subsequently identified ~80 new genes with detectable transcripts. These transcripts, however, are degraded before being exported from the nucleus; thus, highlighting the role of post-transcriptional degraders and regulators. This paper includes many details about the detectable regions and the dynamics of splicing that I didn't mention. If you're working on fission yeast, I strongly suggest this paper.

Mapping Gene-conversion During Meiosis

Source: Mancera et al (2008). High-resolution mapping of meiotic crossovers and non-crossovers in yeast. Nature 454(24):479-485.

The strict linkage between the genes on a chromosome is broken through recombination. In addition to its evolutionary significance, meiosis crossovers also facilitate chromosome segregation. Gene conversion happens either through crossovers (which involves reciprocal exchange of strands) or non-crossover events (through synthesis-dependent strand annealing). In this paper, the authors have presented a high-resolution map of these events. The method (shown in the figure below) involves high-resolution genotyping of all the four viable spores from the meiosis event.


Then, they use the genotyping data to find both crossover and non-crossover conversions (Part b of the figure above from the original paper). The resulting map can be probed for the identification of recombination hotspots. The reported hotspots in this study include almost all of the previously known regions. Interestingly, crossover and non-crossover events have distince distributions with different hotspots.

The authors also test recombination methods to elucidate the role of different pathways in the obsereved deregulations. In the end, they also make an interesting case for interference and how crossover and non-crossover events also show spatial avoidance.

Fine-tuning Transcription Level: Differential Expression of Genes in an Operon

Source: Pfleger et al (2006). Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nat Biotech 24(8):1027-1032.

Coordinated expression of genes active in a pathway or a protein complex is very important with respect to maximizing output and minimizing toxic intermediate compunds. In prokaryotes, related genes are clustered in operons and further fine-tuned through intergenic sequences (e.g. premature termination or RBS masking). While evolution has taken care of endogenous pathways, we have a hard time achieving such an optimality with engineered pathways. In this paper, the authors generate a library of random intergenic regions (which they call TIGRs). These TIGRs may include random hair-pin sites or RNase E sites. They initially test these TIGR libraries on an operon with RFP and GFP genes (see the figure below from the original paper). Screening this library shows a range of both absolute and relative expression levels for these two genes. For example, they show that in regions where the RBS for the second gene is captured in a stem-loop structure the expression of the second gene is drastically reduced. The same holds for the cases where the hair-pin structures prematurely terminate transcription.


Upon making the case for the applicability of their method, they employ it for optimizing an exogenousy introduced mevalonate pathway in E. coli which includes an operon of three genes. Upon screening for higher mevalonate production, the authors identify constructs with upto a seven-fold increase in production.

Protein Binding Microarrays: Finding Transcrition Factor Binding Sites

Source: Berger et al (2006). Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat Biotech 24(11):1429-1435.

When I published a synopsis from Erandi on discovering AP2 binding sites in P. falciparum, I knew at some point I have to write about PBMs. De Silva et al. (2008) have used this ingenious method and apparently it works. The basic idea is that in many cases, we have identified a putative transcription factor and we are interested in finding its binding site (and subsequently identifying its potential targets). This method starts by the purification of an epitope-tagged version of our protein which is then hybridized to a microarray slide harboring all possible k-mers of a given length (e.g. 10-mers). Upon washing away the non-specific interactions, a fluorophore-conjugated antibody (against the epitope-tag) is used to find the spots containing protein-DNA interactions. The final step involves overlapping the bound oligos and finding the consensus binding sequence.

As you see, the theory is simple; however, making the PBM is not trivial given the space of all possible sequences. Below, I have attached a figure from the original paper demonstrating this method. First, they use the notion of de Bruijn sequences to minimize the number of spots needed to represent all possible k-mers. Upon synthesizing these oligos on a slide, they convert them to dsDNA (Cy3 labeled dUTP) using a Cy5 labeled universal primer. The labels are used to ensure that the reactions are in fact completed. An Alexa488-conjugated GST antibody is then used to identify the proteins bound to these features.

Revealing the Chromatin Structure of Human Promoters

Source: Ozsolak et al. (2007). High-throughput mapping of the chromatin structure of human promoters. Nat Biotech 25(2):244-248.

There are many models surrounding the effects of chromatin remodeling and how it affects the genetic context in which the genes are transcribed and expressed. However, without a precise map of where the nucleosomes are, we cannot tell whether they are remodeled or not given a certain stimulus. This paper represents a series of similar studies using tiling arrays (or more recently high-throughput sequencing) to find the nucleosome positionings. Subjecting nucleosome-bound DNA to micrococcal nuclease (MNase) will result in the degradation of the linker fragments while the bound segments will be protected by the histones. In theory, finding the sequence of the fragments surviving MNase should give us the nucleosome positions in the genome. Tiling arrays are suitable for this purpose, helping us to find the parts of the genome that remains intact after digestion.

In practice, the data is way noisier than we might assume due to many reasons (e.g. DNA bound to proteins other than nucleosomes or simply shortcomings in the technical methods). The significance of this paper is in developing computational methods for cancelling out the noise and improving the signal to noise ratio. They effectively succeed in recapitulating the nucleosome positionings that are already known in case of certain promoters (e.g. see the figure below).


Upon mapping the nucleosome positions, the authors proceed to make useful observations. For example, they show that in the highly expressed genes, the promoter region is stripped off the nucleosomes. They also make the case that certain binding elements fall outside of nucleosome-bound region, meaning they are probably bound by active transcription factors.

Coding the Genetic Code: Evolved Ribosomes with Enhanced Capacity in the Expansion of Genetic Code

Source: Wang et al. (2007). Evolved orthogonal ribosomes enhance the efficiency of synthetic genetic code expansion. Nat biotech 25(7):770-777.

The canonical genetic code can be expanded... it includes the incorporation of an unnatural amino acid into a target protein through assignment of a stop codon (e.g. the amber site UAG). Although this has been done (and in some sense has revolutionized biotechnology), there are major obstacles in the way of achieving a high level of incorporation especially in cases where there are more than one occurences of the unnatural codon in the coding sequence. This is in turn due to the competitive activity of the RF-1 protein which terminates translation upon recognition of the amber sites. Evidently, RF-1 is an essential gene and knocking it out is not an option. The authors in this paper come up with a decent idea for circumventing RF-1.

Genetic code expansion is achieved through introduction of an orthogonal tRNA and AA-tRNA synthetase; orthogonal in the sense that the natural AA-tRNA synthetases don't charge the introduced tRNA and the orthogonal AA-tRNA synthetase does not recognize any other tRNAs. In this paper, Wang et al. have taken this notion one step further: introducing an orthogonal ribosome which is unique for translating the target RNA and doesn't bind RF-1. They evolve such ribosome through mutagenizing 16S rRNA and then selecting for a clone that can efficiently read through an amber mutation in order to grow on chloramphenicole (see the figure below). In the rest of the paper, they attempt to validate the activity of this ribosome (which the call Ribo-X).

Got Headache?

Source: Campillos et al (2008). Drug Target Identification Using Side-Effect Similarity. Science 321:263-266.

Living organisms are highly complex systems and when a foreign molecule is introduced (i.e. injected) into the body, non-specific bindings may occur that result in unwanted (and sometimes harmful) side-effects. Target prediction is usually done through structural similarities; where similar compounds affect identical targets. However, two completely different molecules may also share targets which is overlooked in this classic method. The authors of this paper combine the strength of structure-based similarities with symptoms-based knowledge to draw more conclusive predictions. They start with ~700 drugs and extract their reported side-effects. They remove the inherent redundancy (e.g. vomiting and nausea largely overlap) and also normalize the probability of sharing a target based on the total number of occurrences (e.g. dizziness is quite common but other side-effect are more specific).

Following their prediction step, they also validate 20 predicted drugs with significant probabilities of sharing a target (note that all of these drugs are selected so that they represent different classes of drugs with varying therapeutic purposes). They succeed in validating 13/20 of these cases using in vitro and in vivo experiments which is quite impressive.

Graded Expression of A Protein Under Positive Selection

Source: Neuenschwander et al. (2008). A simple selection strategy for evolving highly efficient enzymes. Nat Biotech 25 (10): 1145-47.

This paper discusses a more robust method for directed evolution. Many enzymes can be subjected to evolution if they can be engineered into a background where their activity is essential for growth and survival of the host. This approach works find but there are a number of obstacles. For example, growth is a broad phenotype; even a limited activity of the enzyme might be sufficient to sustain growth resulting in the saturation of the evolution process. In this paper, he authors make the case that by a gradual decrease in the transcription, translation and half-life of the protein of interest, we can reduce the concentration of the available enzyme thus pushing it back into the optimization process. As shown below, their experimental set-up includes an inducible tet promoter and an ssrA for protein degradation and lowerig half life.

Multiplex Color Coded Probe Pairs to Replace Microarrays

Source: Geiss et al. (2008). Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat biotech 26(3):317-325.

This is a very cool paper with an exciting approach... The authors claim that their method is more sensitive compared to chip-based microarrays. Their method involves synthesizing two probes (a capture probe and a reporter probe). The capture probe is simply a biotinylated oligo with a 35-50 complementary region to the target mRNA; whereas, the reporter probe, in addition to a complementary region contains a series of sequences that can be targeted by colored probes. The sequence of these colors along the reporter oligo is unique for each mRNA (Figure a).

The pair of probes for each target is added to the mRNA solution along with the colored probes. Upon the formation of the complex, the oligos are bound to a surface from the biotin group on the capture probe and subjected to a electric field to extend all the molecules to alinear format. The resulting image is then analysed and the number of transcripts for each type is counted (see Figures b-c).


This method (as cool as it seems) is quite expensive and the fact that it is more sensitive does not make it a viable protocol. Specially, with the current emergence of RNA-seq it seems difficult to establish a competitive method.

Modifying the Translation Efficiency: The Role of Codon Pair Biases

Source: Coleman et al (2008). Virus Attenuation by Genome-Scale Changes in Codon Pair Bias. Science 320: 1784-1787.

While there is a huge number of possibilities for two adjacent codons, many of them rarely happen and some of them occur more frequently than predicted by chance alone. This distribution of codon frequencies, in part, can be explained by the amino acid usages in the proteins; however, even synonymous codons show drastically different codon-pair biases. Although the actual mechanism is not known, these biases are believed to affect translation efficiency.

In this paper, the authors use the concept of codon pair biases to synthesize polioviruses with maximum or minimum codon-pair biases. For example, in the min version, for every two amino acids they choose the least frequent codon pair. They subsequentlu show that these altered codon-pair usages drastically affects the virulence of polio virus. These viruses are highly attenuated can be actually used as vaccines to boost the immunity of the tested rats.

The Path to Pluripotency: Dissecting the Reprogrammed Cells

Source: Mikkelsen et al (2008). Dissecting direct reprogramming through integrative genomic analysis. Nature 454: 49-55.

I am not really familiar with this field but I found this paper interesting in the sense that it employs whole-genome methods to tackle a specific and important problem. Human and mouse cells can be transformed back into a pluripotent stage (termed iPS cells) through re-expression of specific transcription factors. However, the success rate of this method is low and the molecular characteristics of this transition is poorly understood.

The first step in reprogramming involves the ectopic expressin of Oct4, Sox2, Klf4 and c-Myc transcription factors. De-differentitation and proliferation of the cells is marked by a decrease in the expression of tissue-specific genes (in this case Snai1 and Snai2) and an increase in DNA replication and cell-cycle progression genes. A parallel boost in the expression of anti-proliferative genes suggests that the mechanism to inhibit uncontrolled prliferation is intact.

Reprogrammed iPS cells, by large, share the expression of key genes with the embryonic stem cells. In this study, however, the authors have extended this similarity to the chromatin structure as well. Much of the details, in this paper, come from studying the partially reprogrammed cells. For example, MCV8, a cell line achieved as a byproduct of an unsuccessful reprogramming, can give rise to both iPS and differentiated cells with an inherent stochasticity involved.

Based on their observations, the authors argue that the cells fail to reprogram because:

1. The cells may induce anti-proliferative genes in response to proliferative stress;
2. They may inappropriately activate or fail to repress endogenous or ectopic transcription factors, and become ‘trapped’ in differentiated states;
3. They may fail to reactivate hypermethylated pluripotency genes.

The bottom line is the fact that reprogramming can be facilitated through another layer of gene regulation (besides the initial four transcription factors).

Fine-tuning Transcription Level: Cyclical Methylation of Promoters

Source: Kangaspeska et al. (2008). Transient cyclical methylation of promoter DNA. Nature 452:112-115.

Apart from nuclear compartmentalization (see previous post), effect of transcription factors and histone modifications, another general regulation mechanism of gene expression exists which transcedes the generations and carries the epigenetic information. Methylation at CpG sites in the promoters is a known repressor of transcription; these methylations however portray a rather static landscape of gene regulation; i.e. until now. Authors of this paper make the case for a dynamic scene regarding methylation status at the CpG dinucleotides. Their results (e.g. see the figure below) show a cyclical methylation and de-methylation of pS2 promoter with a periodocity of ~100 minutes (compared to the constitutively expressed promoter PPIA).The role of this sinuoidal methylation/de-methylation is unknown but one can envisage many key properties that may emerge from this behavior. For example, a general circadian rhythm may be in charge, thus controling the time-scale in which the genes are expressed relative to each other. Moreover, this rhythmic expression, in fact, is capable of increasing the efficiency by which the transcript levels can be controlled.

Repression by Exile: Repositioning to Nuclear Lamina Represses Expression

Source: Reddy et al. (2008). Transcriptional repression mediated by repositioning of genes to the nuclear lamina. Nature 452:243-247.

The correlation between nuclear compartmentalization and gene expression is long known; however, we don't know whether the observed repression of transcription is a by-product of switching compartments or they simply co-occur for other reasons. This paper makes the case for the former; the authors design a construct that can be attached to nuclear membrane upon induction. The reporter set-up (shown below) involves a hygromycin resistance gene (Tk-hyg) as well as multiple copies of Lac operators (lacO) that constitute binding sites for the E. coli Lac repressor (LacI). A GFP-LacI-ΔEMD which is tethered to the nuclear membrane through ΔEMD can be induced by IPTG to recruite lacO sites on the reporter. The localization of the reporter can be simultaneouly monitored using a GFP-LacI fusion.

Using this set-up the authors make the case for a general repression mechanism through membrane tethering. They also employ other methods (e.g. FISH and DamID) to further validate their results which I don't get into.