Leading into my work...

Source: Lisec, J., Meyer, R.C., Steinfath, M., Redestig, H., Becher, M., Witucka-Wall, H., Fiehn, O., Torjek, O., Selbig, J., Altmann, T., and Willmitzer, L. Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations. 2008. The Plant Journal, 53: 960-72

The authors created a number of RIL and IL lines in Arabidopsis and then ran targeted GC-MS on them. They were able to measure 181 compounds and find QTLs for 84, for a total of 157 QTLs. The contribution of these loci was between 1.7 and 52.1%. They found that many of these metabolites co-mapped, and that in nearly all of them a good candidate gene could be found that might explain the effect. They defined a candidate gene as a gene within the support interval in the direct pathway of the metabolite. None of these metabolite linkages showed a strong ability to change biomass.



Other notes:
-permutation test for candidate gene: randomly assign linkage to metabolite, sort through interval and see if any genes overlap with metabolite in AraCyc
=most metabolites showed no significance
=only 13 metabolites showed a higher than permutation-average number of candidate genes
-near impossible to find epistasis, found it only explained 2.72% of phenotypic variation on average
-nonrandom distribution of mQTLs, does not correlate with distribution of metabolic genes

Speed-genotyping

Source: Lai, C-Q., Leips, J., Zou, W., Roberts, J.F., Wollenberg, K.R., Parnell, L.D., Zeng, Z-B., Ordovas, J.M., and Mackay, T.F.C. Speed-mapping quantitative trait loci using microarrays. 2007. Nature Methods, 4(10): 839-41

The authors used microarrays to genotype a large number of individuals for a QTL study into longevity. Instead of individually genotyping and measuring the phenotype, the authors instead selected a subset of the population based on their phenotype (longevity). Then they pooled this subset’s DNA and ran it across a microarray that had oligos from both parents. They compared each marker hybridization with a young group that should be equally mixed for the alleles at each marker. A simple t-test was computed for each marker (with FDR correcting) to determine whether that marker had a skewed allele ratio between samples. Multiple QTLs were found, more so than using previous genotyping methods.

Noise propagation in transcription networks

Source: Dunlop et al (2008). Regulatory activity revealed by dynamic correlations in gene expression noise. Nature Genetics 40(12):1493-1498.

Biological events are stochastic in nature. Random fluctuations in protein concentration, expression and etc relays noise through the transcription network via the regulatory links. For example, a random decrease in the concentration of a repressor results in an increase in the expression of its target gene; however, only if the concentration of the repressor falls within an "active" range in which the expression of the target genes is sensitive to small chanages in the repressor content (see Fig. below).
Thus, observed correlations between the expression of different genes may be the result of a direct or indirect regulatory process. However, in addition to intrinsic noise (fluctuations in the expression of a given gene), we should also consider the extrinsic noise in which all the genes are uniformly affected by a given change (e.g. a random increase in the ribosome content of the cell increases the expression of all the genes). Extrinsic noise causes false positive correlation (see Fig. below).
Thus, any measurement of correlations must be normalized by the effect of extrinsic noises. In this paper, the authors use both stochastic modeling and experimental validation to make the case for this phenomenon.

MISSING: ATP!!

Source: Kresnowati, M.T.A.P., van Winden, W.A., Almering, M.J.H., ten Pierick, A., Ras, C., Knijnenburg, T.A., Daran-Lapujade, P., Pronk, J.T., Heijnen, J.J., and Daran, J.M. When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation. 2006. Molecular Systems Biology, 49

The authors subjected yeast held at steady-state low-glucose levels to a pulse of glucose and recorded their transcriptional and metabolic differences five minutes after the pulse. The most shocking discovery was the remarkable drop in AXP levels, led mainly by ATP. ATP was not simply converted to ADP, nor were AXPs converted for RNA incorporation, over 80% of AXP was unaccounted for after the pulse. Additionally, early-glycolytic metabolites climbed after the pulse but later-glycolytic metabolites sharply dropped. This was explained by the observed jump in NADH/NAD which would inhibit glyceraldehyde-3-phosphate dehydrogenase. With the switch from gluconeogenesis to glycolysis, these later compounds would flush into TCA or ethanol production but not be replenished until redox equilibrium in the cell was returned. On the transcriptome front, over 1000 genes were found to differ between at least two time points, differences didn’t begin until after 120s, though most until after 210s. The upregulated genes were enriched for ribosome biogenesis, amino acid metabolism and purine synthesis, all of the genes leading to adenine production through de novo synthesis, RNA degradation, sulfur metabolism, and conversion. The downregulated genes were enriched for C1-metabolism, energy reserves, and TCA. Additionally a number of genes in those pathways were found to have an order of magnitude lower half-lives for transcripts, from ~30 minutes to four! Looking at 3’, post-stop codon regions, the degraded genes nearly all shared in at least one of four regions that were abundantly found compared to chance.



Other notes:
-1154 genes significantly change
=K-means clustering into 5 groups
-CXP, UXP, and GXP levels also dipped but not on the same magnitude of AXP
-TCA intermediates increased, except citrate
=probably two separate branches: TCA and glyoxylate cycle
=TCA genes downregulated, glyoxylate genes upregulated


So yeah, it's cool that 1/6th of the genome changes its transcription. And yeah, it's interesting that there's an 8-fold difference in transcript half-lives. But WHERE DOES ALL THE ATP GO?!?! In case you're new to biology: ATP is one of the top 10 most used molecules (by number of reactions). This is like saying that upon the introduction to oxygen, humans lose 80% of their red blood cells and no one can see any dead red blood cells, they just vanish. If anyone knows any follow up studies that solved this conundrum, please send my way!

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