The Next Locus is Always Harder

Source: Brem, R.B., Storey, J.D., Whittle, J., and Kruglyak, L. Genetic interactions between polymorphisms that affect gene expression in yeast. 2005. Nature, 436(7051): 701-3

The authors took the transcript data for yeast and searched for interacting loci. They found that the best approach was to search for primary loci, then separate segregants based on allele inherited at that locus and search jointly for another locus. Using this approach, they found that not a large amount of transcripts had detectable interactions and those that did were isolated. One hotspot did appear for loci-pairs, the MAT-GPA relation, these genes are involved in mating type and pheromone response respectively. They used this hotspot to test their interaction theory, creating four engineered strains with the combination of alleles, but identical background, and comparing them to similar-genotyped segregants, finding that most of the phenotypes had parallel responses.

•Other notes:
-identified locus pairings for 225 transcripts
-65% had interaction based on model and FDR
•test on 547 transcripts with two independent loci showed only 13% interacting
-only 33% of secondary loci would have been picked up in a genome scan originally
-hypothesize that half of transcripts are controlled by multiple genetic interactions but that at least one partner has effects too low for mapping



Dual-linking transcripts are plotted in 2-D, with their primary linkage locus on the x-axis, and their secondary on the y-axis. Circles are proportional (in misleading width, NOT area) by the number of transcripts in each 2-D bin. The largest is the GPA-MAT region.

Small Phenotypes - Large Number of Loci

Source: Brem, R.B. and Kruglyak, L. The landscape of genetic complexity across 5,700 gene expression traits in yeast. 2005. PNAS, 102 (5): 1572-7

The authors measured transcript abundance between BY4716 and RM11-1a yeast strains as well as their segregants to do QTL mapping. Over half the transcripts linked to a QTL, but extensive modeling showed that most traits would have multiple loci and no single locus would have a large effect. The authors limited the transcripts they looked at based on heritability scores. They also did a breakdown of inheritance patterns for the phenotypes and concluded that an overwhelming proportion are inherited in a transgressive segregation manner as compared to an additive effect. A small but nontrivial were found to be epistatically inherited, and most were in the transgressive segregation camp.

Other notes:
-median heritability was 27%
-no QTLs detected for nearly 40% of highly heritable transcripts
-only 3% of highly heritable transcripts have single-locus inheritance, 17-18% for one or two loci, over half for at least five loci
-11% had directional model (phenotype of one parent)
-59% were transgressive segregation (outside parental range)
-16% epistatic (tests difference between means of segregants and parents)
-“opposing QTLs may be a mechanism for generating diversity in subsequent generations”


Left: Directional; Center: Transgressive; Right: Epistatic




Damn you genetic complexity!

Discovering microRNA–target RNA pairs

Source: German et al. (2008). Global identification of microRNA–target RNA pairs by parallel analysis of RNA ends. Nature Biotech 26:941-946.

The authors have introduced a great method for identifying miRNA target sites through sequencing the 3' cleavage fragments and finding their potential miRNAs. The schematics of their method is shown below where they ligate a linker to the 3' cleavage fragments. Normal mRNAs would not be ligated due to the presence of a cap or lack of 5' phosphate. They subsequently amplify and sequence the ligated fragments. Their library has a great coverage and encompasses ~90% of the orfs in yeast. Their method captures 98 out 100 known miRNA target sites in arabidopsis. This batch of sequenced target sites were then used to identify the corresponding miRNA. They validate a set of their prediction which seems to work fine.

Good-bye stress response

Source: Brauer, M.J., Huttenhower, C., Airoldi, E.M., Rosenstein, R., Matese, J.C., Gresham, D., Boer, V.M., Troyanskaya, O.G., and Botstein, D. Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. 2008. Molecular Biology of the Cell, 19: 352-67

The authors ran 36 samples (6 growth rates on 6 nutrient limitations, including two auxotrophs) and measured global transcript levels at and steady-stage growth. They looked to see if gene expression could be a linearly correlated with growth rate and found that for 27% of genes, this appeared to be the case. Auxotrophs limited for phosphate or nitrogen showed cell arrest and normal levels of glucose and ethanol in the media, but when limited for their auxotrophy showed high fermentation with greatly enhanced levels of ethanol present. The authors hypothesize that there are two metabolic controls: one that’s independent of nutrient type, limits based on cell growth without full arrest while the other senses a natural nutrient starvation and fully arrests the cell. The authors also compare their gene sets of positively and negatively correlated genes to gene sets from stress response, cell cycle periodicity and yeast metabolic cycle periodicity. They find that most of the genes positively correlated with growth rate are found in stress repressed conditions and mitochondrial and cytosolic ribosome clusters from YMC. While genes negatively correlated to growth rate are found in stress induced and peroxisomal clusters from YMC. They suggest that these genes are mainly responding to instantaneous growth rates and not the particular condition tested. Building a model off this presumption, they are able to more-or-less recreate the growth rate from transcript data from Brauer et al 2005 involving the diauxic shift. Much of their significance for whether a gene is positively or negatively-correlated with growth comes from bootstrapping.


Figure 5. Transcriptional response of stress-related and cell cycle-related genes to changes in growth rate. x-axis: Slope of transcript abudance vs growth. Genes expressed periodically during the cell cycle (black line; Spellman et al., 1998) are distributed essentially as background, whereas genes induced (red line) or repressed (green line) by stress (Gasch et al., 2000) tend to be conversely repressed or induced as growth rate increases.

• Other notes:
-positively-correlated genes are enriched for ribosomal proteins
-auxotrophs don’t arrest when lacking their auxotrophic nutrient, but do when lacking a natural nutrient; also consume twice as much glucose
-5537 genes measured, 3049 fit linear model with growth rate, 1470 respond significantly to growth rate
-can play with sets of genes at http://growthrate.princeton.edu
=GO enrichment – negative: energy metabolism, oxidative metabolism, oxioreductase activity, peroxisome
=positive: mitochondrial protein import, translation, ribosome biogenesis, rRNA metabolism
-ESR genes may be responding not to stress but to instantaneous growth rate
-cell-cycle genes had same distribution as total gene set
=only M-G1 phase was slightly enriched for negative correlation with growth rate
•slower growing cells spend more time waiting for division signal
-yeast grown in batch on poorer nutrient sources had better stress response
=this supports the theory that ESR-induced genes are just slow-rate genes, these cells were already growing slowly so they did not change as dramatically

Although I find it intriguing to think that transcript abundance is more a response to growth rate than stress, I find some problems with that interpretation. Slower growing cells, goes the hypothesis, will respond better to stress since they already have the right transcript profile. BUT, slower growing cells spend a greater proportion of their life in G1 and G2, the energy-producing stages. This means they are already subject to more stress per cell cycle, and thus that is the reason the Stress/Slow Growth profile is already up. In the end, maybe it's semantics unless we can start to find stress-specific profiles.

How harmful are amino acid changes?

Source: Boyko, A.R., Williamson, S.H., Indap, A.R., Degenhardt, J.D., Hernandez, R.D., Lohmueller, K.E., Adams, M.D., Schmidt, S., Sninsky, J.J., Sunyaev, S.R., White, T.J., Nielsen, R., Clark, A.G., and Bustamante, C.D. Assessing the evolutionary impact of amino acid mutations in the human genome. 2008. PLoS Genetics, 4(5):e1000083

Its well-known that amino acids aren't necessarily well-conserved in homologous proteins, but function will remain. Obviously some amino acids can easily substitute in for others to fulfill some structure or charge function. So what happens when a SNP comes along and alter an amino acid? Usually, nothing, or at most a slight decrease in stability.

The authors scanned the human genome of a small sample of African and European Americans, looking for SNPs, allele frequency, and the amount of change each SNP may cause. They found that many nonsynonymous mutations are neutral (27-29%), a large amount are slightly deleterious (30-42%), and the rest are highly deleterious or lethal. Most of the highly deleterious alleles were present in alleles that were less than 5% of sampled alleles at that locus. Additionally, the cumulative affect of benign mutations greatly outweighed the effect of harmful mutations. The affect of SNPs was measured using PolyPhen, a program that characterized the SNP as benign, possibly damaging, and probably damaging based on its conservation and amino acid change; ‘damaging’ only referring to protein structure not organisms’ fitness. Furthermore, comparing to an outgroup of chimpanzees, 10-20% of SNPs were deemed fixed by positive selection.


Allele frequency on x-axis

Red: strongly deleterious
Orange: moderately
Yellow: weakly
Green: nearly neutral
Blue: neutral
White: beneficial

Other notes:
-around half of nonsynonymous mutations are strongly or mildly deleterious
-most segregating variation above 5% frequency in the population is predicted to be nearly neutral, with higher proportion of neutral variation as the allele frequency increases
-15,916 benign; 4,199 possibly damaging; 2,646 probably damaging SNPs from PolyPhen
-estimated 5% of benign, 27% of possibly, and 35% of probably damaging were fixed through positive selection

RNA-Seq: Deep Sequencing the Human Transcriptome

Source: Sultan et al. (2008). A Global View of Gene Activity and Alternative Splicing by Deep Sequencing of the Human Transcriptome. Science 321:956-960.

Apparently, we're going back to re-doing all the experiments that we once did using tiling arrays. These papers are far from innovative but I think it's good for the people in the field to know that this data is out there (hence supporing its publication in science). Apart from expensive reactions, tiling arrays also miss vital information like splice sites and alternative splicing. RNA-seq circumvents most of the problems inherent to other methods and the authors have used this approach to provide snapshots of human transcriptome at the nucleotide level. The authors do many controls to validate their results. For example, they compare the reads for each gene to PolII Chip-seq data to make the case that the number of reads is a good measure for expression. The figure below (from the original paper) shows the distribution of PolII Chip-seq reads for the genes with different levels of expression (the x-asis is the position relative to TSS).


Their results generally include the identification 25% more genes and ~100, 000 splice sites (compared to ~4000 known before). They also show that exon skipping is the most prevalent method of splicing.

Metabolic Flux and Sequence Conservation

Source: Bilu, Y., Shlomi, T., Barkai, N., and Ruppin, E. Conservation of expression and sequence of metabolic genes is reflected by activity across metabolic states. 2006. PLoS Computational Biology, 2(8):e106

The authors use flux balance analysis (FBA) to create an optimal solution set for metabolic genes in various media conditions. They then compare the flux in this set for individual reactions to evolution rate of the protein-coding region, promoter, and expression levels. The authors found rough correlation in the expected manner: lower flux variability genes show higher conservation across species. Additionally, genes found to be active throughout different media conditions were found to be well conserved.

Other notes
-in FBA’s optimal solution space there are missing constraints to the model, though it has been shown that models still carry meaningful biological info
-flux values show moderate, statistically significant correlation with corresponding gene expression levels and protein abundance
-flux variability analysis: difference between max and min flux values in the space of optimal flux distributions for each reaction
-expression divergence score: expression patterns among four yeast strains
-promoter conservation score: based on three yeast species’ conservation of S. cerevisiae transcription factor binding sites
-moderate, statistically significant correlation between flexibility scores and expression divergence score, promoter conservation score
-possible that high flexibility score means the genes are in pathways that have alternatives
-of the 50 genes with highest flex score, 6% are essential, whereas 17% of the 89 zero-flex genes are essential
-statistically significant correlation between knockout growth rate (For metabolic genes) and evolutionary rate
-best predictor for evolutionary rate is the expression level of the encoded gene
-“Previous studies suggest that the predicted variability in metabolic states may represent heterogeneous metabolic behaviors of individuals within a cell population”


I found this study short and sweet. A simple question: Do highly conserved genes have more rigid optimum activities? So for enzymes that are not metabolically constrained we see a greater diversity in sequence. This makes senses, mess with the choke-points and you screw up a pathway, tinker with some of the non rate-limiting genes and there isn't as much a problem.