Yearly Archives: 2014

Author post: Facilitated diffusion buffers noise in gene expression

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This guest post is by Radu Zabet on his preprint (with Armin Schoech) Facilitated diffusion buffers noise in gene expression, arXived here.

How does the binding dynamics of transcription factors affect the noise in gene expression?

Transcription factors (TFs) are proteins that bind to DNA and control gene activity. Gene regulation can be modelled as a chemical reaction, which is fundamentally a stochastic process. Given the importance of an accurate control of the gene regulatory program in the cell, significant efforts have been made in understanding the noise properties of gene expression.

Why can noise in gene expression be modelled assuming an ON/OFF gene model?

With few exceptions, previous studies investigated the noise in gene expression assuming that the regulatory process is a two-state Markov model (genes switch stochastically between ON and OFF states). However, it is known that, mechanistically, transcription factors find their genomic target sites through facilitated diffusion, a combination of 3D diffusion in the cytoplasm/nucleoplasm and 1D random walk along the DNA, and this is likely to influence the noise properties of the gene regulation process. Previous experimental studies (e.g. see http://www.nature.com/ng/journal/v43/n6/full/ng.821.html) successfully modelled the noise measured experimentally by assuming an ON/OFF gene model (two-state Markov model) in bacterial and animal cells. In this manuscript, we built a three-state Markov model that accurately models the facilitated diffusion and we showed that for biologically relevant parameters, at least in bacteria (we assumed lac repressor system http://www.sciencemag.org/content/336/6088/1595), noise in gene expression can be modelled assuming the ON/OFF gene model, but only if the binding/unbinding rates are adjusted accordingly. This explains why in many cases the experimental noise in gene regulation can be modelled assuming an ON/OFF gene model. Note that there are several exceptions where the noise in gene expression does not seem to be accounted by the ON/OFF gene model (e.g. http://genome.cshlp.org/content/early/2014/07/16/gr.168773.113 or http://www.pnas.org/content/111/29/10598).

What is the effect of facilitated diffusion on the noise in gene expression?

Next, assuming the ON/OFF gene model we investigated the evolutionary advantage that a TF, which performs facilitated diffusion, has on noise in gene expression compared to an equivalent TF that only performs the 3D diffusion (and does not perform 1D random walk on the DNA). Our results show that the noise in gene expression can be reduced significantly when the TF performs facilitated diffusion compared to its equivalent TF that only performs 3D diffusion in the cell. This is important, because while the majority of the studies identify the speedup in the binding site search process as the main evolutionary advantage of why facilitated diffusion exists, we show that, in addition to this speedup in binding kinetics, facilitated diffusion also reduces the noise in gene expression. Interestingly, it seems that the noise level in gene expression is reduced close to the noise level of an unregulated gene (the lowest noise level in gene expression that could be achieved), while the noise of an equivalent TF that performs only 3D diffusion is significantly higher.

Finally, to test our model, we parameterise it with values measured experimentally in the case of lac repressor in E. coli and we estimated the mean mRNA level to be 0.16 and the Fano factor (variance divided by mean) to be 1.3 (as opposed to 2.0 in the case of TF performing only 3D diffusion). These values are similar to the values measured experimentally in the low inducer case of Plac by http://www.nature.com/ng/journal/v43/n6/full/ng.821.html (mean mRNA level of 0.15 and Fano factor of 1.25) and shows that facilitated diffusion is essential in explaining the experimentally measured noise in mRNA.

Author post: Sharing of Very Short IBD Segments between Humans, Neandertals, and Denisovans

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This guest post is by Gundula Povysil and Sepp Hochreiter on their preprint Sharing of Very Short IBD Segments between Humans, Neandertals, and Denisovans, bioRxived here.

We completed our preprint Sharing of Very Short IBD Segments between Humans, Neandertals, and Denisovans in bioRxiv by presenting results not only for chromosome 1 but now for all autosomes and chromosome X.

In this manuscript we analyze the sharing of very short identity by descent (IBD) segments between humans, Neandertals, and Denisovans to gain new insights into their demographic history. In the updated version we included a separate chromosome X analysis (both IBD segment sharing and length of segments). We identified IBD segments in the 1000 Genomes Project sequencing data using our recently published method HapFABIA, many of which are shared with Neandertals or Denisovans.

Here we highlight the most interesting findings of our analysis:

Introgression from Denisovans into ancestors of Asians:

The Denisova genome most prominently matches IBD segments that are shared by Asians and on average these segments are longer than segments shared between other continental populations and the Denisova genome. Therefore, we could confirm an introgression from Denisovans into ancestors of Asians after their migration out of Africa.

Introgression from Neandertals into ancestors of Europeans and Asians:

While Neandertal-matching IBD segments are most often shared by Asians, Europeans share a considerably higher percentage of IBD segments with Neandertals compared to other populations, too. Neandertal-matching IBD segments that are shared by Asians or Europeans are longer than those observed in Africans. These IBD segments hint at a gene flow from Neandertals into ancestors of Asians and Europeans after they left Africa.

Ancient Neandertal and Denisova IBD segments survived only in Africans

Interestingly, many Neandertal- and/or Denisova-matching IBD segments are predominantly observed in Africans – some of them even exclusively. IBD segments shared between Africans and Neandertals or Denisovans are strikingly short, therefore we assume that they are very old. Consequently, we conclude that DNA regions from ancestors of humans, Neandertals, and Denisovans have survived in Africans.

Neandertal but no Denisova introgression on the X chromosome

Neandertal-matching IBD segments on chromosome X confirm gene flow from Neandertals into ancestors of Asians and Europeans outside Africa. Interestingly, there is hardly any signal of Denisova introgression on the X chromosome.

We highly appreciate any comments, discussions, or thoughts on our results.

Butter: High-precision genomic alignment of small RNA-seq data

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Butter: High-precision genomic alignment of small RNA-seq data
Michael J Axtell

Eukaryotes produce large numbers of small non-coding RNAs that act as specificity determinants for various gene-regulatory complexes. These include microRNAs (miRNAs), endogenous short interfering RNAs (siRNAs), and Piwi-associated RNAs (piRNAs). These RNAs can be discovered, annotated, and quantified using small RNA-seq, a variant RNA-seq method based on highly parallel sequencing. Alignment to a reference genome is a critical step in analysis of small RNA-seq data. Because of their small size (20-30 nts depending on the organism and sub-type) and tendency to originate from multi-gene families or repetitive regions, reads that align equally well to more than one genomic location are very common. Typical methods to deal with multi-mapped small RNA-seq reads sacrifice either precision or sensitivity. The tool ‘butter’ balances precision and sensitivity by placing multi-mapped reads using an iterative approach, where the decision between possible locations is dictated by the local densities of more confidently aligned reads. Butter displays superior performance relative to other small RNA-seq aligners. Treatment of multi-mapped small RNA-seq reads has substantial impacts on downstream analyses, including quantification of MIRNA paralogs, and discovery of endogenous siRNA loci. Butter is freely available under a GNU general public license.

Facilitated diffusion buffers noise in gene expression

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Facilitated diffusion buffers noise in gene expression

Armin Schoech, Nicolae Radu Zabet
(Submitted on 22 Jul 2014)

Transcription factors perform facilitated diffusion (3D diffusion in the cytosol and 1D diffusion on the DNA) when binding to their target sites to regulate gene expression. Here, we investigated the influence of this binding mechanism on the noise in gene expression. Our results showed that, for biologically relevant parameters, the binding process can be represented by a two-state Markov model and that the accelerated target finding due to facilitated diffusion leads to a reduction in both the mRNA and the protein noise.

Clonal interference and Muller’s ratchet in spatial habitats

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Clonal interference and Muller’s ratchet in spatial habitats
Jakub Otwinowski, Joachim Krug
(Submitted on 18 Feb 2013 (v1), last revised 23 Jul 2014 (this version, v3))

Competition between independently arising beneficial mutations is enhanced in spatial populations due to the linear rather than exponential growth of clones. Recent theoretical studies have pointed out that the resulting fitness dynamics is analogous to a surface growth process, where new layers nucleate and spread stochastically, leading to the build up of scale-invariant roughness. This scenario differs qualitatively from the standard view of adaptation in that the speed of adaptation becomes independent of population size while the fitness variance does not. Here we exploit recent progress in the understanding of surface growth processes to obtain precise predictions for the universal, non-Gaussian shape of the fitness distribution for one-dimensional habitats, which are verified by simulations. When the mutations are deleterious rather than beneficial the problem becomes a spatial version of Muller’s ratchet. In contrast to the case of well-mixed populations, the rate of fitness decline remains finite even in the limit of an infinite habitat, provided the ratio Ud/s2 between the deleterious mutation rate and the square of the (negative) selection coefficient is sufficiently large. Using again an analogy to surface growth models we show that the transition between the stationary and the moving state of the ratchet is governed by directed percolation.

Statistical and conceptual challenges in the comparative analysis of principal components

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Statistical and conceptual challenges in the comparative analysis of principal components

Josef C Uyeda, Daniel S. Caetano, Matthew W Pennell

Quantitative geneticists long ago recognized the value of studying evolution in a multivariate framework (Pearson, 1903). Due to linkage, pleiotropy, coordinated selection and mutational covariance, the evolutionary response in any phenotypic trait can only be properly understood in the context of other traits (Lande, 1979; Lynch and Walsh, 1998). This is of course also well?appreciated by comparative biologists. However, unlike in quantitative genetics, most of the statistical and conceptual tools for analyzing phylogenetic comparative data (recently reviewed in Pennell and Harmon, 2013) are designed for analyzing a single trait (but see, for example Revell and Harmon, 2008; Revell and Harrison, 2008; Hohenlohe and Arnold, 2008; Revell and Collar, 2009; Schmitz and Motani, 2011; Adams, 2014b). Indeed, even classical approaches for testing for correlated evolution between two traits (e.g., Felsenstein, 1985; Grafen, 1989; Harvey and Pagel, 1991) are not actually multivariate as each trait is assumed to have evolved under a process that is independent of the state of the other (Hansen and Orzack, 2005; Hansen and Bartoszek, 2012). As a result of these limitations, researchers with multivariate datasets are often faced with a choice: analyze each trait as if they were independent or else decompose the dataset into statistically independent set of traits, such that each set can be analyzed with the univariate methods.

Concerning RNA-Guided Gene Drives for the Alteration of Wild Populations

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Concerning RNA-Guided Gene Drives for the Alteration of Wild Populations
Kevin M Esvelt, Andrea L Smidler, Flaminia Catteruccia, George M Church

Gene drives may be capable of addressing ecological problems by altering entire populations of wild organisms, but their use has remained largely theoretical due to technical constraints. Here we consider the potential for RNA-guided gene drives based on the CRISPR nuclease Cas9 to serve as a general method for spreading altered traits through wild populations over many generations. We detail likely capabilities, discuss limitations, and provide novel precautionary strategies to control the spread of gene drives and reverse genomic changes. The ability to edit populations of sexual species would offer substantial benefits to humanity and the environment. For example, RNA-guided gene drives could potentially prevent the spread of disease, support agriculture by reversing pesticide and herbicide resistance in insects and weeds, and control damaging invasive species. However, the possibility of unwanted ecological effects and near-certainty of spread across political borders demand careful assessment of each potential application. We call for thoughtful, inclusive, and well-informed public discussions to explore the responsible use of this currently theoretical technology.

Assessing allele specific expression across multiple tissues from RNA-seq read data

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Assessing allele specific expression across multiple tissues from RNA-seq read data
Matti Pirinen, Tuuli Lappalainen, Noah A Zaitlen, GTEx Consortium, Emmanouil T Dermitzakis, Peter Donnelly, Mark I McCarthy, Manuel A Rivas

Motivation: RNA sequencing enables allele specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression project (GTEx) is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data. Results: We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally. Availability: MAMBA software: http://birch.well.ox.ac.uk/~rivas/mamba/ R source code and data examples: http://www.iki.fi/mpirinen/ Contact: matti.pirinen@helsinki.fi rivas@well.ox.ac.uk

Fixation properties of subdivided populations with balancing selection

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Fixation properties of subdivided populations with balancing selection
Pierangelo Lombardo, Andrea Gambassi, Luca Dall’Asta
Comments: 17 pages, 10 figures
Subjects: Populations and Evolution (q-bio.PE); Statistical Mechanics (cond-mat.stat-mech); Biological Physics (physics.bio-ph)

In subdivided populations, migration acts together with selection and genetic drift and determines their evolution. Building up on a recently proposed method, which hinges on the emergence of a time scale separation between local and global dynamics, we study the fixation properties of subdivided populations in the presence of balancing selection. The approximation implied by the method is accurate when the effective selection strength is small and the number of subpopulations is large. In particular, it predicts a phase transition between species coexistence and biodiversity loss in the infinite-size limit and, in finite populations, a nonmonotonic dependence of the mean fixation time on the migration rate. In order to investigate the fixation properties of the subdivided population for stronger selection, we introduce an effective coarser description of the dynamics in terms of a voter model with intermediate states, which highlights the basic mechanisms driving the evolutionary process.

RNA-seq gene profiling – a systematic empirical comparison

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RNA-seq gene profiling – a systematic empirical comparison
Nuno A Fonseca, John A Marioni, Alvis Brazma

Accurately quantifying gene expression levels is a key goal of experiments using RNA-sequencing to assay the transcriptome. This typically requires aligning the short reads generated to the genome or transcriptome before quantifying expression of pre-defined sets of genes. Differences in the alignment/quantification tools can have a major effect upon the expression levels found with important consequences for biological interpretation. Here we address two main issues: do different analysis pipelines affect the gene expression levels inferred from RNA-seq data? And, how close are the expression levels inferred to the “true” expression levels? We evaluate fifty gene profiling pipelines in experimental and simulated data sets with different characteristics (e.g, read length and sequencing depth). In the absence of knowledge of the ‘ground truth’ in real RNAseq data sets, we used simulated data to assess the differences between the true expression and those reconstructed by the analysis pipelines. Even though this approach does not take into account all known biases present in RNAseq data, it still allows to assess the accuracy of the gene expression values inferred by different analysis pipelines. The results show that i) overall there is a high correlation between the expression levels inferred by the best pipelines and the true quantification values; ii) the error in the estimated gene expression values can vary considerably across genes; and iii) a small set of genes have expression estimates with consistently high error (across data sets and methods). Finally, although the mapping software is important, the quantification method makes a greater difference to the results.