Divergent Initiation of Transcription

Source: Core et al. (2008). Nascent RNA sequencing reveals widespread pausing and divergent initiation at human promoters. Science 322:1845-1848.

In the current issue of science (Vol. 322), two back-to-back articles are published both reporting divergent transcription close to TSS. The authors have used global run-on sequencing to determine the site, amount and orientation of active RNApols. One of their main finding is this divergence in transcription.
How is this helpful?
1. Transcription leads to chromatin modifications that may be essential for dynamic expression (see my previous post)
2. Transcription may expose the binding sites that are otherwise engaged in nucleosomes.
3. The resulting negative supercoiling may benefit transcription in the region.

Gene Expresion Regulation: Chromatin Remodelling

Source: Hirota et al. (2008). Stepwise chromatin remodelling by a cascade of transcription initiatoin of non-coding RNAs. Nature 456:130-134.

The RNA-seq strategy has revolutionized our way of doing biology, but it has also complicated the way we used to look at gene expression regulation. First, it has been shown that a huge number of RNAs are produced without ever being translated. Many of these species are envisioned to participate in some sort of expression regulation... In this paper, the authors make the case for one such mechanism: firing from upstream promoters results in chromatin modifications that leads to the activation of the main promoter.

While studying the regulation on fbp 1+ in yeast, the authors observed that upon starvation it takes around 60 min for the main RNA to show up; however, during this period 3 other longer RNAs show up suggesting active upstream promoters (a, b and c in figure below). Using chromatin-IP for RNApolII, they confirmed the occupation of these upstream promoters upon activation. They also assyed chromatin remodelling using MNase assay to show that the chromatin is in fact modified upon activation.
The key point here, however, was the fact that upon cloning a transcription termination site between the upstream promoters and the main promoter inhibits activation... which means transcription is required for the observed chromatin remodelling. The figure below, from the original paper, shows the details of this mechanism.

Correlating Transcription and Cell Cycle

Source: Klevecz, R.R., Bolen, J., Forrest, G., and Murray, D.B. A genomewide oscillation in transcription gates DNA replication and cell cycle. 2004. PNAS, 101(5): 1200-5

The authors measured transcript abundance as it fluctuated with changes in dissolved oxygen content for yeast. They found that there were three timepoints were gene expression peaked: two peaks with >2,000 genes reaching their maximum expression when oxygen levels were high (cells nonrespiring) and one peak where 650 genes reached their maximum expression when oxygen levels were low (cells respiring). Compared transcripts to states, and found that mitochondrial genes are expressed during reductive phase when mitochondrial function is minimal; while sulfur metabolism genes are expressed in respiratory phase right before they are needed for DNA replication in beginning of reductive phase. Most periods were ~40 minutes, and other studies showed that on a variety of media the doubling times of yeast were some multiple of 40 minutes.



•Other notes:
-cell-to-cell synchronization involved through respiratory inhibition by H2S and phase shifts due to acetaldehyde
-87% of genes expressed maximally in reductive phase
=2400 early, 2200 late
-650 genes maximum expression in oxidative phase
-4-12 minute lag between transcript peak and maximum gene product function
-DNA replication begins abruptly at end of respiration, H2S levels rise
-separation in time between oxidative and reductive phases goes to transcript levels and is coordinated with DNA replication
=prevents oxidative stress

Metabogenome: Discovering compounds a genes!

Source: Keurentjes, J.J.B., Fu, J., Ric de Vos, C.H., Lommen, A., Hall, R.D., Bino, R.J., van der Plas, L.H.W., Jansen, R.C., Vreugdenhil, D., and Koornneef, M. The genetics of plant metabolism. 2006. Nature Genetics, 38(7): 842-9

The authors used LC-QTOF MS to create metabolite profiles for two parental strains of Arabidopsis thaliana and 160 RIL descendants. They then genotyped these strains and ran QTL analysis on the >2000 metabolites they measured to come up with a staggering number of potential QTLs. Interestingly, a large number of compounds were not detected in either parent strain but only in the RILs. QTL hotspots were found and a study on a specific hotspot and its linked metabolites’ pathway carried out. This analysis was able to find the relative position in the pathway between two loci. Additionally, an analysis on a hotspot with unknown metabolites gave a set of metabolites to classify. Once discovered, the distinction in phenotypes revealed the presence of a previously unrealized enzyme in one of the parents. The authors close by stating that pathway elucidation and identification are possible through this high-throughput analysis, as well as metabolite grouping for identification.

Other notes:
-75% of compounds were assigned a QTL
-853 of 2129 metabolites not detected in either parent
-QTL for 1592 metabolites, roughly 2 QTLs per compound
-all AOP-related metabolites also map to MAM, while few MAM-metabolites map to AOP suggests AOP is downstream of MAM
-correlation between masses were calculated based on QTL profiles: vectors of P-values associated with markers
-co-occurrence of well-known or unknown metabolites may reveal pathway information

Yeast Epistasis Map

Source: Roguev et al (2008). Conservation and Rewiring of Functional Modules Revealed by an Epistasis Map in Fission Yeast. Science 322:405.

Epistasis analysis is one of the most direct methods for defining functional relationships between genes and proteins. These interactions can be negative (synthetic lethality) or positive (suppression). Whole-genome high-throughput epistatic maps (E-MAP) were peviously published for S. cerevisiae; here, the authors focus on S. pombe. E-MAPs are generated through generating pairwise knock-outs and assaying their henotypes (usually growth in complex media), comparing them to the single-gene mutants. This E-map includes ~118,000 double mutants in 550 genes invloved in different aspects of cellular processes. In this set, similar to previous E-MAPs, the correlation between protein-protein interactions (PPI) and epistasis scores is apparent (see the figure below from the original paper).

The authors have also focused a great deal on dissecting the RNAi machinery in S. pombe. This study resulted in the identification of a novel component in this machinary (rsh1).