Kleckner, N. | Search bioRxiv

bioRxiv2017

DNA sequence homology induces cytosine-to-thymine mutation by a heterochromatin-related pathway

In the genomes of many eukaryotes, including mammals, highly repetitive DNA is normally associated with histone H3 lysine-9 di-/trimethylation (H3K9me2/3) and C5-cytosine methylation (5mC) in the context of heterochromatin. In the fungus Neurospora crassa, H3K9me3 and 5mC are catalyzed, respectively, by a conserved SUV39 histone methylase DIM-5 and a DNMT1-like cytosine methylase DIM-2. Here we show that DIM-2 can also mediate cytosine-to-thymine mutation of repetitive DNA during the pre-meiotic process known as Repeat-Induced Point mutation (RIP) in N. crassa. We further show that DIM-2-dependent RIP requires DIM-5, HP1, and other heterochromatin factors, implying the involvement of a repeat-induced heterochromatin-related process. Our previous findings suggest that the mechanism of homologous repeat recognition for RIP involves direct pairwise interactions between co-aligned double-stranded (ds) DNA segments. Our current findings therefore raise the possibility that such pairing interactions may occur not only in pre-meiotic but also in vegetative cells, where they may direct heterochromatin assembly on repetitive DNA. In accord with this possibility, we find that, in vegetative cells of N. crassa, our model repeat array comprising only four 674-bp sequence copies can trigger a low level of DIM-5-dependent 5meC. We thus propose that homologous dsDNA/dsDNA interactions between a small number of repeat copies can nucleate a transient state of heterochromatin and that, on longer repeat arrays, such interactions lead to the formation of stable heterochromatin. Since the number of possible pairwise dsDNA/dsDNA interactions will scale non-linearly with the number of repeats, this mechanism provides an attractive way of creating the extended domains of constitutive heterochromatin found in pericentromeric and subtelomeric regions.

molecular biology↗

bioRxiv2016

Quantitative Modeling and Automated Analysis of Meiotic Recombination

Many morphological features, in both physical and biological systems, exhibit spatial patterns that are specifically characterized by a tendency to occur with even spacing (in one, two or three dimensions). The positions of crossover (CO) recombination events along meiotic chromosomes provides an interesting biological example of such an effect (1-3). In general, mechanisms that explain such patterns may (a) be mechanically-based, (b) occur by a reaction-diffusion mechanism in which macroscopic mechanical effects are irrelevant, or (c) involve a combination of both types of effects. We have proposed that meiotic CO patterns arise by a mechanical mechanism, have developed mathematical expressions for such a process based on a particular physical system with analogous properties (the so-called \"beam-film model\"), and have shown that the beam-film model can very accurately explain experimental CO patterns as a function of the values of specific defined parameters (4-7). Importantly, the mathematical expressions of the beam-film model can apply quite generally to any mechanism, whether it involves mechanical components or not, as long as its logic and component features correspond to those of the beam-film system (3; below). Furthermore, via its various parameters, the beam-film model discretizes the patterning process into specific components. Thus, the model can be used to explore the theoretically predicted effects of various types of changes in the patterning process. Such predictions can expand detailed understanding of the bases for various biological effects (e.g. 2, 5). We present here a new MATLAB program that implements the mathematical expressions of the beam-film model with increased robustness and accessibility as compared to programs presented previously. As in previous versions, the presented program permits both: (i) simulation of predicted CO positions along chromosomes of a test population; and (ii) easy analysis of CO positions, both for experimental data sets and for data sets resulting from simulations. The goal of the current presentation is to make these approaches more readily accessible to a wider audience of researchers. Also, the program is easily modified, and we encourage interested users to make changes to suit their specific needs. A link to the program is available on the Kleckner laboratory web site: http://projects.iq.harvard.edu/kleckner_lab.

genetics↗