Mechanistic insights into type III restriction enzymes

Type III restriction-modification (R-M) enzymes need to interact with two separate unmethylated DNA sequences in indirectly repeated, head-to-head orientations for efficient cleavage to occur at a defined location next to only one of the two sites. However, cleavage of sites that are not in head-to-head orientation have been observed to occur under certain reaction conditions in vitro. ATP hydrolysis is required for the long-distance communication between the sites prior to cleavage. Type III R-M enzymes comprise two subunits, Res and Mod that form a homodimeric Mod(2) and a heterotetrameric Res(2)Mod(2) complex. The Mod subunit in M-2 or R2M2 complex recognizes and methylates DNA while the Res subunit in R2M2 complex is responsible for ATP hydrolysis, DNA translocation and cleavage. A vast majority of biochemical studies on Type III R-M enzymes have been undertaken using two closely related enzymes, EcoP1I and EcoP15I. Divergent opinions about how the long-distance interaction between the recognition sites exist and at least three mechanistic models based on 1D- diffusion and/or 3D-DNA looping have been proposed.

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called `early warning signals', and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data.