Source: Ingolia et al (2009). Genome-Wide Analysis in Vivo of Translation with Nucleotide
Resolution Using Ribosome Profiling. Science 324: 218-223.
Proteomics is the game, but due to technical difficulties in the direct quantification of protein types in the cell, we have instead been using transcriptome measurements as a proxy for protein expression. This is a decent proxy but far from perfect. In this paper, the authors bring us a step closer to protein quantification through measuring the portion of transcriptome that is actually being translated. They combine the ribosome-mediated protection of traslated RNA molecules with the power of deep sequencing to determine the ribosome positionings at a single nt resolution.
What the authors found?
1. There is an excess of ribosomes bound to the first 30-40 codons; a quantity which drops substantially in the later codons.
2. uORFs are quite widespread, resulting in a high ribosome presence in the 5'UTR.
Monday, April 20, 2009
Tuesday, March 31, 2009
Revealing Genetic Interactions in E. coli
Source: Typas et al, (2008). High-throughput, quantitative analyses of genetic interactions in E. coli. Nature Methods 5, 781 - 787.
Genetic interaction studies in bacteria are quite challenging. Before this study, we had data for hundreds of interactions in E. coli in comparison with thousands known for yeast. In this paper, the authors introduce a method termed GIANT which relies on massive Hfr conversion and double mutant generation in E. coli. The method is presented below as a figure from the original paper:
Here, we rely on conjugation to select for double markers (double mutants) and assay they growth on 384 or 1536 colony arrays. The authors make the case for their method through several validation steps. In the end, this method has the ability to generalize to other organisms for which deletion collections are available.
Genetic interaction studies in bacteria are quite challenging. Before this study, we had data for hundreds of interactions in E. coli in comparison with thousands known for yeast. In this paper, the authors introduce a method termed GIANT which relies on massive Hfr conversion and double mutant generation in E. coli. The method is presented below as a figure from the original paper:
Here, we rely on conjugation to select for double markers (double mutants) and assay they growth on 384 or 1536 colony arrays. The authors make the case for their method through several validation steps. In the end, this method has the ability to generalize to other organisms for which deletion collections are available.Friday, March 13, 2009
Proliferation-resistant biotechnology
Source: Nouri A., Chyba C., 2009. Proliferation-resistant biotechnology: an approach to improve biological security. Nature Biotechnology 27, 234 - 236.
A dear friend of mine Ali Nouri (also a AAAS congressional fellow) has published an interesting commentary in the current issue of Nature Biotech. Generally, we (the scientists) sometimes fail to grasp the security implications of science. Now, while the uninhibited progression of science is essential for our prosperity and avoiding regression back to another dark age, we should also try to find inexpensive and applivable ways to boost security.
Making an entire organism from scratch is not a dream anymore. This has been done in case of many viruses. The resurrection of the 1918 influenza virus caused a turmoil in our field. While we learned alot about the virus (e.g. how close it actually is to avian flu), many questioned whether this type of research should be prohibited. Personally, I don't think any type of basic research should be prohibited because any thing may simply revolutionize our lives, but I agree that this information should be protected against misuse and abuse.
In this commentary, the authors have simply requetsed the companies to screen their bulk requests and raise a red flag if the requested gene or genome belongs to a list of dangerous oragnsisms or toxins. Steps as simple as this are very cheap to implement. And I'm sure many of you are already coming up with solutions for potential bypass of this problem. But if we put enough obstacles in the way of misusing these technologies, the accumulative security would actually synergistically increase and may very well pass the threshold for many ill-willed individuals.
A dear friend of mine Ali Nouri (also a AAAS congressional fellow) has published an interesting commentary in the current issue of Nature Biotech. Generally, we (the scientists) sometimes fail to grasp the security implications of science. Now, while the uninhibited progression of science is essential for our prosperity and avoiding regression back to another dark age, we should also try to find inexpensive and applivable ways to boost security.
Making an entire organism from scratch is not a dream anymore. This has been done in case of many viruses. The resurrection of the 1918 influenza virus caused a turmoil in our field. While we learned alot about the virus (e.g. how close it actually is to avian flu), many questioned whether this type of research should be prohibited. Personally, I don't think any type of basic research should be prohibited because any thing may simply revolutionize our lives, but I agree that this information should be protected against misuse and abuse.
In this commentary, the authors have simply requetsed the companies to screen their bulk requests and raise a red flag if the requested gene or genome belongs to a list of dangerous oragnsisms or toxins. Steps as simple as this are very cheap to implement. And I'm sure many of you are already coming up with solutions for potential bypass of this problem. But if we put enough obstacles in the way of misusing these technologies, the accumulative security would actually synergistically increase and may very well pass the threshold for many ill-willed individuals.Monday, February 9, 2009
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
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
Monday, February 2, 2009
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.
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.
Wednesday, January 28, 2009
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.
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.
Tuesday, January 13, 2009
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!
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!
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