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.

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.

This guest post is by Karyn Meltz Steinberg on her preprint (with coauthors) Single haplotype assembly of the human genome from a hydatidiform mole, bioRxived here.

The human reference sequence is a mosaic of many DNA sources patched together to create the (mostly) contiguous chromosomes we all use every day in genomics labs. This mélange of haplotypes can result in reference representations that do not exist. For example, in GRCh37 at the MRC1 locus mixed haplotypes led to the presence of two gene models that represent false duplications and a gap that affected alignments of short reads. The problem of diploid source DNA was even worse in regions of structural variation where it was difficult to distinguish allelic variation from paralogous variation. The assembly structure in these regions was often wrong with either a collapsed assembly leading to missing sequence or with haplotype expanse, meaning 2, 3 or more haplotypes were represented on the chromosome sequence. The genomic resources associated with the essentially haploid complete hydatidiform mole, CHM1, have opened the door to allow us to address these issues.

What is essentially haploid? Well, what usually happens is that a sperm (usually bearing an X chromosome) fertilizes an egg that doesn’t have a nucleus (thus no DNA), the sperm DNA doubles, giving the correct chromosome complement, but with both pairs of chromosomes being identical. This cell divides and grows but does not form a normal embryo. In the early 1990s, Dr. Urvashi Surti was able to make an immortalized cell line from CHM1 tissue using hTERT. She karyotyped each passage to check that it maintained ploidy and that there were no gross somatic rearrangements.

Dr. Pieter de Jong then created an indexed BAC library from this high fidelity material. These BACs could then be used to resolve structurally complex regions such as 17q21.311. We continued working on sequencing more tiling paths across structurally complex regions; however, it was not practical or cost-efficient to Sanger sequence every single clone in the BAC library. As work continued, it became clear that developing the Primary Assembly using a single haplotype resource could be very powerful. This was possible due to the efforts of the Genome Reference Consortium (GRC) to extend the assembly model to include more than one sequence representation for a give region. We used Illumina sequencing technology and a reference based assembly algorithm developed at NCBI to produce an initial assembly. We then integrated the BAC sequences into the assembly to improve regions that are nearly impossible to assembly using whole genome strategies. The result is the highest quality whole genome sequence human genome assembly that is publicly available to date as assessed by metrics including contig and scaffold N50, repetitive element content and gene annotation.

So what–how can this help us, you ask? For the first time, one single haplotype of the human genome is represented. The fact that CHM1 is haploid means that we are able to finally go into the messy regions of the genome and resolve the genomic architecture as well as put any structural variation in the context of surrounding linked allelic variation. These are often biologically interesting regions; for example genes related to immune response and metabolism that are probably associated with complex traits are usually members of large gene families in segmentally duplicated sequence.

A fine example of the power of this haploid resource is also on BioRxiv, “Sequencing of the human IG light chain loci from a hydatidiform mole BAC library reveals locus-specific signatures of genetic diversity” (disclosure: this is also co-authored by me). The IG light chain genes encode for one part of immunoglobulin molecules that are expressed by B cells in response to antigenic stimulation (the heavy chain, IGH, was also resolved using CHM1 resources last year2). They are part of large gene families formed by duplication at three loci in the human genome. In previous versions of the reference assembly, these loci were comprised of sequence from multiple DNA sources that may have undergone somatic rearrangement. By sequencing BAC clones in a tiling path across these loci we now have a single haplotype representation of germline DNA sequence that allows us to perform accurate analyses of variation.

REFERENCES

1 Itsara, A. et al. Resolving the breakpoints of the 17q21.31 microdeletion syndrome with next-generation sequencing. American journal of human genetics 90, 599-613, doi:10.1016/j.ajhg.2012.02.013 (2012).
2 Watson, C. T. et al. Complete haplotype sequence of the human immunoglobulin heavy-chain variable, diversity, and joining genes and characterization of allelic and copy-number variation. American journal of human genetics 92, 530-546 (2013).