Summary Plant metabolism is broadly reprogrammed during acclimation to abiotic changes. Most previous studies have focused on transitions from standard to single stressful conditions. Here, we systematically analyze acclimation processes to levels of light, heat, and cold stress that subtly alter physiological parameters and assess their reversibility during de-acclimation. Metabolome and transcriptome changes were monitored at 11 different time points. Unlike transcriptome changes, most alterations in metabolite levels did not readily return to baseline values, except in the case of cold acclimation. Similar regulatory networks operate during (de-)acclimation to high light and cold, whereas heat and high-light responses exhibit similar dynamics, as determined by surprisal and conditional network analyses. In all acclimation models tested here, super-hubs in conditional transcriptome networks are enriched for components involved in translation, particularly ribosomes. Hence, we suggest that the ribosome serves as a common central hub for the control of three different (de-)acclimation responses.
The NRF2 (also known as NFE2L2) transcription factor is a critical regulator of genes involved in defense against oxidative stress. Previous studies suggest that Nrf2 plays a role in adipogenesis in vitro, and deletion of the Nrf2 gene protects against diet-induced obesity in mice. Here, we demonstrate that resistance to diet-induced obesity in Nrf2 ؊/؊ mice is associated with a 20 -30% increase in energy expenditure. Analysis of bioenergetics revealed that Nrf2 ؊/؊ white adipose tissues exhibit greater oxygen consumption. White adipose tissue showed a >2-fold increase in Ucp1 gene expression. Oxygen consumption is also increased nearly 2.5-fold in Nrf2-deficient fibroblasts. Oxidative stress induced by glucose oxidase resulted in increased Ucp1 expression. Conversely, antioxidant chemicals (such as N-acetylcysteine and Mn(III)tetrakis(4-benzoic acid) porphyrin chloride) and SB203580 (a known suppressor of Ucp1 expression) decreased Ucp1 and oxygen consumption in Nrf2-deficient fibroblasts. These findings suggest that increasing oxidative stress by limiting Nrf2 function in white adipocytes may be a novel means to modulate energy balance as a treatment of obesity and related clinical disorders.
Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays. The deep neural network is able to learn a regression model that relates the physicochemical properties of a peptide to its ion intensity detected by mass spectrometry. The approach makes use of experimentally detected deviations from the assumed equimolar abundance of all peptides derived from a given protein. Trained on extensive proteomics datasets, d::pPop's plant and non-plant specific models can predict the quality of proteotypic peptides for not yet experimentally identified proteins. Interrogating the deep neural network after learning from ~76,000 peptides per model organism allows to investigate the impact of different physicochemical properties on the observability of a peptide, thus providing insights into peptide observability as a multifaceted process. Empirical evaluation with rank accuracy metrics showed that our prediction approach outperforms existing algorithms. We circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the need for selecting the top most promising peptides for targeting a protein of interest. Further, we used an artificial QconCAT protein to experimentally validate the observability prediction. Our proteotypic peptide prediction approach not only facilitates the design of absolute protein quantification assays via a user-friendly web interface but also enables the selection of proteotypic peptides for not yet observed proteins, hence rendering the tool especially useful for plant research.
In Neurospora crassa, a circadian rhythm of conidiation (asexual spore formation) can be seen on the surface of agar media. This rhythm has a period of 22 hr in constant darkness (D/D). Under constant illumination (L/L), no rhythm is visible and cultures show constant conidiation. However, here we report that strains with a mutation in the vivid (
Maintenance of a balanced redox state within the cell is of critical importance to a wide variety of biological systems. Nuclear factor erythroid-derived 2-like 2 (Nrf2) is a critical regulator of key aspects of the antioxidant defense pathway and has long been a subject of interest regarding conditions of chronic stress such as inflammation and cancer. Recent data have emerged demonstrating that oxidative stress and Nrf2 also play critical roles in the biology of adipose tissue. This review examines data identifying the roles of Nrf2 and oxidative stress in the biological process of adipose cell differentiation as well as the implications of Nrf2 modulation on obesity. Working to understand the complex interplay among Nrf2, oxidative stress, and adipose biology could lead to a variety of possible treatments for obesity and other related disorders.
In Neurospora, the circadian rhythm is expressed as rhythmic conidiation driven by a feedback loop involving the protein products of frq (frequency), wc-1 (white collar-1), and wc-2, known as the frq/wc (FWC) oscillator. Although strains carrying null mutations such as frq 10 or wc-2D lack a functional FWC oscillator and do not show a rhythm under most conditions, a rhythm can be observed in them by the addition of geraniol or farnesol to the media. Employing this altered media as an assay, the effect of other clock mutations in a frq 10 -or wc-2D-null background can be measured. It was found that the existing clock mutations fall into three classes: (1) those, such as prd-3 or prd-4 or frq 1 , that showed no effect in a clock null background; (2) those, such as prd-1 or prd-2 or prd-6, that did have a measurable effect in the frq 10 background; and (3) those, such as the new mutation ult, that suppressed the frq 10 or wc-2D effect, i.e., geraniol/farnesol was not required for a visible rhythm. This classification suggests that some of the known clock mutations are part of a broader multioscillator system.
Matrix targeting sequences (MTSs) direct proteins from the cytosol into mitochondria. Efficient targeting often relies on internal matrix targeting-like sequences (iMTS-Ls) which share structural features with MTSs. Predicting iMTS-Ls was tedious and required multiple tools and webservices. We present iMLP, a deep learning approach for the prediction of iMTS-Ls in protein sequences. A recurrent neural network has been trained to predict iMTS-L propensity profiles for protein sequences of interest. The iMLP predictor considerably exceeds the speed of existing approaches. Expanding on our previous work on iMTS-L prediction, we now serve an intuitive iMLP webservice available at http://iMLP.bio.uni-kl.de and a stand-alone command line tool for power user in addition.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.