Biology subjects

Evans, M. E.

Publications and source records attributed to Evans, M. E..

2 recordsLinked to original sources

bioRxiv2021

SPIRE, Surface Projection Image Recognition Environment for bicontinuous phases: application for plastid cubic membranes

Bicontinuous membranes in cell organelles epitomise natures ability to create complex functional nanostructures. Like their synthetic counterparts, these membranes are characterised by continuous membrane sheets draped onto topologically complex saddle-shaped surfaces with a periodic network-like structure. In cell organelles, their structure sizes around 50-500 nm and fluid nature make Transmission Electron Microscopy (TEM) the analysis method of choice to decipher nanostructural features. Here we present a tool to identify bicontinuous structures from TEM sections by comparison to mathematical "nodal surface" models, including the hexagonal lonsdaleite geometry. Our approach, following pioneering work by Deng and Mieczkowski (1998), creates synthetic TEM images of known bicontinuous geometries for interactive structure identification. We apply the method to the inner membrane network in plant cell chloroplast precursors and achieve a robust identification of the bicontinuous diamond surface as the dominant geometry in several plant species. This represents an important step in understanding their as yet elusive structure-function relationship.

cell biology↗

bioRxiv2018

Estimating RNA structure chemical probing reactivities from reverse transcriptase stops and mutations.

Chemical probing experiments interrogate RNA structures by creating covalent adducts on RNA molecules in structure-dependent patterns. Adduct positions are then detected through conversion of the modified RNAs into complementary DNA (cDNA) by reverse transcription (RT) as either stops (RT-stops) or mutations (RT-mutations). Statistical analysis of the frequencies of RT-stops and RT-mutations can then be used to estimate a measure of chemical probing reactivity at each nucleotide of an RNA, which reveals properties of the underlying RNA structure. Inspired by recent work that showed that different reverse transcriptase enzymes show distinct biases for detecting adducts as either RT-stops or RT-mutations, here we use a statistical modeling framework to derive an equation for chemical probing reactivity using experimental signatures from both RT-stops and RT-mutations within a single experiment. The resulting formula intuitively matches the expected result from considering reactivity to be defined as the fraction of adduct observed at each position in an RNA at the end of a chemical probing experiment. We discuss assumptions and implementation of the model, as well as ways in which the model may be experimentally validated.

biochemistry↗