Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition.

Quantitative proteomics strategies - which are playing important roles in the expanding field of plant molecular systems biology - are traditionally designated as either hypothesis driven or non-hypothesis driven. Many of these strategies aim to select individual peptide ions for tandem mass spectrometry (MS/MS), and to do this mixed hypothesis driven and non-hypothesis driven approaches are theoretically simple to implement. In-depth investigations into the efficacies of such approaches have, however, yet to be described. In this study, using combined samples of unlabeled and metabolically (15)N-labeled Arabidopsis thaliana proteins, we investigate the mixed use of targeted data acquisition (TDA) and data dependent acquisition (DDA) - referred to as TDA/DDA - to facilitate both hypothesis driven and non-hypothesis driven quantitative data collection in individual LC-MS/MS experiments. To investigate TDA/DDA for hypothesis driven data collection, 7 miRNA target proteins of differing size and abundance were targeted using inclusion lists comprised of 1558 m/z values, using 3 different TDA/DDA experimental designs. In samples in which targeted peptide ions were of particularly low abundance (i.e., predominantly only marginally above mass analyser detection limits), TDA/DDA produced statistically significant increases in the number of targeted peptides identified (230 ± 8 versus 80 ± 3 for DDA; p = 1.1 × 10(-3)) and quantified (35 ± 3 versus 21 ± 2 for DDA; p = 0.038) per experiment relative to the use of DDA only. These expected improvements in hypothesis driven data collection were observed alongside unexpected improvements in non-hypothesis driven data collection. Untargeted peptide ions with m/z values matching those in inclusion lists were repeatedly identified and quantified across technical replicate TDA/DDA experiments, resulting in significant increases in the percentages of proteins repeatedly quantified in TDA/DDA experiments only relative to DDA experiments only (33.0 ± 2.6% versus 8.0 ± 2.7%, respectively; p = 0.011). These results were observed together with uncompromised broad-scale MS/MS data collection in TDA/DDA experiments relative to DDA experiments. Using our observations we provide guidelines for TDA/DDA method design for quantitative plant proteomics studies, and suggest that TDA/DDA is a broadly underutilized proteomics data acquisition strategy

A conditional silencing suppression system for transient expression.

RNA silencing is a powerful tool deployed by plants against viral infection and abnormal gene expression. Plant viruses have evolved a suite of silencing suppressors for counter-defense, which are also widely used to boost transcript and protein accumulation in transient assays. However, only wild type silencing suppressor proteins have been reported to date. Here we demonstrate that P0 of Potato leafroll virus (PLRV), PLP0, can be split into two proteins that only show silencing suppression activity upon co-expression. We cloned each of these proteins in two different constructs and transiently co-infiltrated them in N. benthamiana leaves. We expressed a fluorescent protein from one of the vectors and observed that cells expressing both halves of PLP0 suppressed gene silencing. Further, we showed that Q system of Neurospora crassa, based on co-expression of a transcription activator and inhibitor, is functional in agroinfiltrated leaves of N. benthamiana. Q system combined with the split PLP0 system showed very tight co-expression of Q system's transcriptional activator and inhibitor. Altogether, our experiments demonstrate a functioning conditional silencing suppressor system and its potential as a powerful tool for transient expression in N. benthamiana leaves, as well as the application of the Q system in plants

A multi-omic Nicotiana benthamiana resource for fundamental research and biotechnology

Nicotiana benthamiana is an invaluable model plant and biotechnology platform with a ~3 Gb allotetraploid genome. To further improve its usefulness and versatility, we have produced high-quality chromosome-level genome assemblies, coupled with transcriptome, epigenome, microRNA and transposable element datasets, for the ubiquitously used LAB strain and a related wild accession, QLD. In addition, single nucleotide polymorphism maps have been produced for a further two laboratory strains and four wild accessions. Despite the loss of five chromosomes from the ancestral tetraploid, expansion of intergenic regions, widespread segmental allopolyploidy, advanced diploidization and evidence of recent bursts of Copia pseudovirus (Copia) mobility not seen in other Nicotiana genomes, the two subgenomes of N. benthamiana show large regions of synteny across the Solanaceae. LAB and QLD have many genetic, metabolic and phenotypic differences, including disparate RNA interference responses, but are highly interfertile and amenable to genome editing and both transient and stable transformation. The LAB/QLD combination has the potential to be as useful as the Columbia-0/Landsberg errecta partnership, utilized from the early pioneering days of Arabidopsis genomics to today

A Semi-Infinite Programming based algorithm for determining T-optimum designs for model discrimination

T-optimum designs for model discrimination are notoriously difficult to find because of the computational difficulty involved in solving an optimization problem that involves two layers of optimization. Only a handful of analytical T-optimal designs are available for the simplest problems; the rest in the literature are found using specialized numerical procedures for a specific problem. We propose a potentially more systematic and general way for finding T-optimal designs using a Semi-Infinite Programming (SIP) approach. The strategy requires that we first reformulate the original minimax or maximin optimization problem into an equivalent semi-infinite program and solve it using an exchange-based method where lower and upper bounds produced by solving the outer and the inner programs, are iterated to convergence. A global Nonlinear Programming (NLP) solver is used to handle the subproblems, thus finding the optimal design and the least favorable parametric configuration that minimizes the residual sum of squares from the alternative or test models. We also use a nonlinear program to check the global optimality of the SIP-generated design and automate the construction of globally optimal designs. The algorithm is successfully used to produce results that coincide with several T-optimal designs reported in the literature for various types of model discrimination problems with normally distributed errors. However, our method is more general, merely requiring that the parameters of the model be estimated by a numerical optimization

Measurements of the production cross-section for a Z boson in association with b- or c-jets in proton–proton collisions at √s = 13 TeV with the ATLAS detector

This paper presents a measurement of the production cross-section of a Z boson in association with bor c-jets, in proton–proton collisions at √s = 13 TeV with the ATLAS experiment at the Large Hadron Collider using data corresponding to an integrated luminosity of 140 fb−1. Inclusive and differential cross-sections are measured for events containing a Z boson decaying into electrons or muons and produced in association with at least one b-jet, at least one c-jet, or at least two b-jets with transverse momentum pT > 20 GeV and rapidity |y| < 2.5. Predictions from several Monte Carlo generators based on next-to-leading-order matrix elements interfaced with a parton-shower simulation, with different choices of flavour schemes for initial-state partons, are compared with the measured cross-sections. The results are also compared with novel predictions, based on infrared and collinear safe jet flavour dressing algorithms. Selected Z+ ≥ 1 c-jet observables, optimized for sensitivity to intrinsic-charm, are compared with benchmark models with different intrinsic-charm fractions

The ATLAS trigger system for LHC Run 3 and trigger performance in 2022

The ATLAS trigger system is a crucial component of the ATLAS experiment at the LHC. It is responsible for selecting events in line with the ATLAS physics programme. This paper presents an overview of the changes to the trigger and data acquisition system during the second long shutdown of the LHC, and shows the performance of the trigger system and its components in the proton-proton collisions during the 2022 commissioning period as well as its expected performance in proton-proton and heavy-ion collisions for the remainder of the third LHC data-taking period (2022–2025)

Measurement of diferential cross-sections in tt¯ and tt¯+jets production in the lepton+jets fnal state in pp collisions at √s = 13 TeV using 140 fb−1 of ATLAS data

Diferential cross-sections for top-quark pair production, inclusively and in association with jets, are measured in pp collisions at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC using an integrated luminosity of 140 fb−1. The events are selected with one charged lepton (electron or muon) and at least four jets. The differential cross-sections are presented at particle level as functions of several jet observables, including angular correlations, jet transverse momenta and invariant masses of the jets in the final state, which characterise the kinematics and dynamics of the top-antitop system and the hard QCD radiation in the system with associated jets. The typical precision is 5%–15% for the absolute differential cross-sections and 2%–4% for the normalised differential cross-sections. Next-to-leading-order and next-to-next-to-leading-order QCD predictions are found to provide an adequate description of the rate and shape of the jet-angular observables. The description of the transverse momentum and invariant mass observables is improved when next-to-next-to-leading-order QCD corrections are included