Biology subjects

Pierson, E.

Publications and source records attributed to Pierson, E..

3 recordsLinked to original sources

bioRxiv2026

Evaluating β-glucanases as cell wall-permeabilising agents against Phytophthora agathidicida oospores

The persistence of specialised survival spores produced by microbial pathogens represents a primary bottleneck in the management of plant diseases. In oomycetes, these spores (known as oospores) are largely impervious to chemical control, allowing them to persist in soil and initiate new infection cycles over many years. A prominent example is the soil-borne pathogen Phytophthora agathidicida, the causal agent of kauri dieback disease, where long-lived oospores hinder conservation efforts in native forests. The resilience of oospores is attributed to their thick wall composed of complex {beta}-glucan layers that render the oospores impermeable to most conventional biocides. Here we have investigated an enzyme-based approach for weakening the oospore cell wall. We searched enzyme databases to select {beta}-glucanases targeting a variety of linkages found in Phytophthora oospore walls. Eight of these {beta}-glucanases were successfully purified and tested for their digestive activity against intact oospores in vitro using a phenol-sulfuric acid assay. We showed that combining these enzymes was crucial to achieve significant digestion through synergies and additive effects. The optimal combination, comprising 1,3-, 1,6-, and 1,3(4)-{beta}-glucanases, was evaluated for its ability to permeabilise oospores to five biocides typically effective only on other, more sensitive lifecycle stages of the pathogen. Using a live/dead fluorescence assay, we observed that the effects of the membrane-targeting biocides were potentiated in oospores that were pre-treated with the {beta}-glucanase mixture. Our results highlight enzymatic cell wall permeabilisation as a promising strategy toward improved management of oospore persistence in kauri forest soils and against broader oomycete threats. KeypointsO_LIOur phenol-sulfuric acid assay can be used to screen for oospore-degrading enzymes. C_LIO_LISynergistic enzyme combinations are essential for effective oospore wall digestion. C_LIO_LIEnzyme pre-treatment sensitises oospores to membrane-targeting biocides. C_LI

biochemistry↗

bioRxiv2019

The menstrual cycle is a primary contributor to cyclic variation in women’s mood, behavior, and vital signs

Female mood, behavior, and vital signs exhibit cycles which fundamentally affect health and happiness. However, it is unclear which dimensions of mood, behavior, and vital signs vary cyclically, how cycles at different timescales compare in magnitude, and how cycles vary across countries. Here we separate female mood, behavior, and vital signs into four simultaneous cycles - daily, weekly, seasonal, and menstrual. We analyze nine mood dimensions, three behavior dimensions, and three vital signs using a dataset of 241 million observations from 3.3 million women in 109 countries. We find that the menstrual cycle is a primary contributor to cyclic variation: it is the cycle with the largest amplitude for all three vital signs, sexual activity, and 7 out of 9 dimensions of mood. The amplitude of the menstrual happiness cycle is 1.4x the amplitude of the daily cycle, 3.3x the amplitude of the weekly cycle, 2.3x the amplitude of the seasonal cycle, and 1.7x the Christmas increase in happiness. Menstrual cycle effects are directionally consistent across countries, demonstrating that they occur across cultures. Overall, our results demonstrate the primacy of the menstrual cycle and necessitate better accounting for it in clinical data and practice.

physiology↗

bioRxiv2017

SIMLR: A Tool For Large-Scale Single-Cell Analysis By Multi-Kernel Learning

MotivationWe here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a cell-to-cell similarity measure from single-cell RNA-seq data. SIMLR can be effectively used to perform tasks such as dimension reduction, clustering, and visualization of heterogeneous populations of cells. SIMLR was benchmarked against state-of-the-art methods for these three tasks on several public datasets, showing it to be scalable and capable of greatly improving clustering performance, as well as providing valuable insights by making the data more interpretable via better a visualization.\n\nAvailability and ImplementationSIMLR is available on GitHub in both R and MATLAB implementations. Furthermore, it is also available as an R package on bioconductor.org.\n\nContactbowang87@stanford.edu or daniele.ramazzotti@stanford.edu\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗