Norris, J. | bioRxiv Science

bioRxiv2022

Mechanistic Insight into AP-Endonuclease 1 Cleavage of Abasic Sites at Stalled Replication Forks

1.Many types of DNA damage stall replication fork progression, including abasic sites. AP-Endonuclease 1 (APE1) has been shown to cleave abasic sites in ssDNA substrates. Importantly, APE1 cleavage of ssDNA at a replication fork has significant biological implications by generating double strand breaks that could collapse the replication fork. Despite this, the molecular basis and efficiency of APE1 processing abasic sites at a replication fork remains elusive. Here, we investigate APE1 cleavage of several abasic substrates that mimic potential APE1 interactions at replication forks. We determine that APE1 has robust activity on these substrates, similar to dsDNA, and report rapid rates for cleavage and product release. X-ray crystal structures visualize the APE1 active site, highlighting that a similar mechanism is used to process ssDNA substrates as canonical APE1 activity on dsDNA. However, mutational analysis reveals R177 to be uniquely critical for the APE1 ssDNA cleavage mechanism. Additionally, we investigate the interplay between APE1 and Replication Protein A (RPA), the major ssDNA-binding protein at replication forks, revealing that APE1 can cleave an abasic site while RPA is still bound to the DNA substrate. Together, this work provides molecular level insights into abasic ssDNA processing by APE1, including the presence of RPA.

biochemistry↗

bioRxiv2022

Partitioned Local Depth analysis of time course transcriptomic data reveals elaborate community structure

Transcriptome studies that provide temporal information are valuable for identifying groups of similarly-behaving transcripts, giving insight into overarching gene regulatory networks. Nevertheless, inferring transcriptional networks from time series data is challenging, in part because it is difficult to holistically consider both local relationships and global structure of these complex and overlapping transcriptional responses. To address this need, we employed the Partitioned Local Depth (PaLD) method to examine four time series transcriptomic datasets generated using the model plant Arabidopsis thaliana. Here, we provide a self-contained description of the method and demonstrate how it can be used to make predictions about gene regulatory networks based on time series data. The analysis provides a global network representation of the data from which graph partitioning methods and neighborhood analysis can reveal smaller, more well-defined groups of like-responding transcripts. These groups of transcripts that change in response to hormone treatment (e.g., auxin or ethylene) or high salinity were demonstrated to be enriched in common biological function and/or binding of transcription factors that were not identified with prior analyses of this data using other clustering and inference methodologies. These results reveal the ability of PaLD to generate predictions about gene regulatory networks using time series transcriptomic data, which can be of value to the systems biology community.

systems biology↗