Biology subjects

Webb, A. I.

Publications and source records attributed to Webb, A. I..

18 recordsLinked to original sources

bioRxiv2022

Mass Dynamics 2.0: An improved modular web-based platform for accelerated proteomics insight generation and decision making

Data processing is essential to reliably generate knowledge from proteomics studies. The complexity of the proteomics data, as well as the ability of research teams to adopt complex analysis pipelines, have proven to be an obstacle to effective collaboration and more efficient biological insight generation. Here, we introduce MD 2.0, a cloud- and web-based platform for quantitative proteomics data, which implements a novel analysis workspace where statistical analyses, visualizations, and external knowledge generation are integrated into a modular framework. This modularity enables researchers the flexibility to test different hypotheses, and customize and template complex proteomics analyses, thereby expediting insight generation for complex datasets. The extensible MD 2.0 environment has been built with a scalable architecture to allow rapid development of future analysis modules and enhanced tools for remote collaboration, like experiment sharing and a live chat capability. The new drag-and-drop modules allow researchers to easily and quickly assess different aspects of an experiment, including quality control, differential expression and enrichment analysis. The modularity of MD 2.0 lays the foundation to support broader community-based analytical template generation and optimized sharing and collaboration between proteomics experts and biologists, thereby accelerating research teams abilities to extract knowledge from complex proteomics datasets.

bioinformatics↗

bioRxiv2022

A de novo MS1 feature detector for the Bruker timsTOF Pro

1Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptides fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursors mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptides peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706). 2 Author SummaryIn the identification of peptides in mass spectrometry data, much attention has been given to the targeting and extraction of mass spectra produced by fragmentation of precursor ions. However, important information about the peptide is revealed by the data representing the precursor ion itself, such as the peptides charge state, mass-to-charge ratio, intensity, and retention time. The timsTOF produces the additional dimension of ion mobility, which provides richer information about the precursor. Although tools exist for the analysis of timsTOF data, they are hampered by limited dynamic range. In this work, we describe a de novo feature detector called 3DID that detects peptide features across the full mass range. Our detector can detect more peptides than existing tools across a broader range of abundance, which enables more comprehensive analysis of the data. We believe 3DID will make a valuable contribution to the proteomics toolbox.

bioinformatics↗

bioRxiv2022

Predicting coordinates of peptide features in raw timsTOF data with machine learning for targeted extraction reduces missing values in label-free DDA LC-MS/MS proteomics experiments

1The determination of relative protein abundance in label-free data dependant acquisition (DDA) LC-MS/MS proteomics experiments is hindered by the stochastic nature of peptide detection and identification. Peptides with an abundance near the limit of detection are particularly effected. The possible causes of missing values are numerous, including; sample preparation, variation in sample composition and the corresponding matrix effects, instrument and analysis software settings, instrument and LC variability, and the tolerances used for database searching. There have been many approaches proposed to computationally address the missing values problem, predominantly based on transferring identifications from one run to another by data realignment, as in MaxQuants matching between runs (MBR) method, and/or statistical imputation. Imputation transfers identifications by statistical estimation of the likelihood the peptide is present based on its presence in other technical replicates but without probing the raw data for evidence. Here we present a targeted extraction approach to resolving missing values without modifying or realigning the raw data. Our method, which forms part of an end-to-end timsTOF processing pipeline we developed called Targeted Feature Detection and Extraction (TFD/E), predicts the coordinates of peptides using machine learning models that learn the delta of each peptides coordinates from a reference library. The models learn the variability of a peptides location in 3D space from the variability of known peptide locations around it. Rather than realigning or altering the raw data, we create a run-specific lens through which to observe the data, targeting a location for each peptide of interest and extracting it. By also creating a method for extracting decoys, we can estimate the false discovery rate (FDR). Our method outperforms MaxQuant and MSFragger by achieving substantially fewer missing values across an experiment of technical replicates. The software has been developed in Python using Numpy and Pandas and open sourced with an MIT license (DOI 10.5281/zenodo.6513126) to provide the opportunity for further improvement and experimentation by the community. Data are available via ProteomeXchange with identifier PXD030706. 2 Author SummaryMissed identifications of peptides in data-dependent acquisition (DDA) proteomics experiments are an obstacle to the precise determination of which proteins are present in a sample and their relative abundance. Efforts to address the problem in popular analysis workflows include realigning the raw data to transfer a peptide identification from one run to another. Another approach is statistically analysing peptide identifications across an experiment to impute peptide identifications in runs in which they were missing. We propose a targeted extraction technique that uses machine learning models to construct a run-specific lens through which to examine the raw data and predict the coordinates of a peptide in a run. The models are trained on differences between observations of confidently identified peptides in a run and a reference library of peptide observations collated from multiple experiments. To minimise the risk of drawing unsound experimental conclusions based on an unknown rate of false discoveries, our method provides a mechanism for estimating the false discovery rate (FDR) based on the misclassification of decoys as target features. Our approach outperforms the popular analysis tool suites MaxQuant and MSFragger/IonQuant, and we believe it will be a valuable contribution to the proteomics toolbox for protein quantification.

bioinformatics↗

bioRxiv2021

PIPP: Improving peptide identity propagation using neural networks

Peptide identity propagation (PIP) can substantially reduce missing values in label-free mass spectrometry quantification by transferring peptides identified by tandem mass (MS/MS) spectra in one run to experimentally related runs where the peptides are not identified by MS/MS. The existing frameworks for matching identifications between runs perform peak tracing and propagation based on similarity of precursor features using only a limited number of dimensions available in MS1 data. These approaches do not produce accompanying confidence estimates and hence cannot filter probable false positive identity transfers. We introduce an embedding based PIP that uses a higher dimensional representation of MS1 measurements that is optimized to capture peptide identities using deep neural networks. We developed a propagation framework that works entirely on MaxQuant results. Current PIP workflows typically perform propagation mainly using two feature dimensions, and rely on deterministic tolerances for identification transfer. Our framework overcomes both these limitations while additionally assigning probabilities to each transferred identity. The proposed embedding approach enables quantification of the empirical false discovery rate (FDR) for peptide identification, while also increasing depth of coverage through coembedding the runs from the experiment with experimental libraries. In published datasets with technical and biological variability, we demonstrate that our method reduces missing values in MaxQuant results, maintains high quantification precision and accuracy, and low false transfer rate.

bioinformatics↗

bioRxiv2021

Simplifying MS1 and MS2 spectra to achieve lower mass error, more dynamic range, and higher peptide identification confidence on the Bruker timsTOF Pro

1For bottom-up proteomic analysis, the goal of analytical pipelines that process the raw output of mass spectrometers is to detect, characterise, identify, and quantify peptides. The initial steps of detecting and characterising features in raw data must overcome some considerable challenges. The data presents as a sparse array, sometimes containing billions of intensity readings over time. These points represent both signal and chemical or electrical noise. Depending on the biological samples complexity, tens to hundreds of thousands of peptides may be present in this vast data landscape. For ion mobility-based LC-MS analysis, each peptide is comprised of a grouping of hundreds of single intensity readings in three dimensions: mass-over-charge (m/z), mobility, and retention time. There is no inherent information about any associations between individual points; whether they represent a peptide or noise must be inferred from their structure. Peptides each have multiple isotopes, different charge states, and a dynamic range of intensity of over six orders of magnitude. Due to the high complexity of most biological samples, peptides often overlap in time and mobility, making it very difficult to tease apart isotopic peaks, to apportion the intensity of each and the contribution of each isotope to the determination of the peptides monoisotopic mass, which is critical for the peptides identification. Here we describe four algorithms for the Bruker timsTOF Pro that each play an important role in finding peptide features and determining their characteristics. These algorithms focus on separate characteristics that determine how candidate features are detected in the raw data. The first two algorithms deal with the complexity of the raw data, rapidly clustering raw data into spectra that allows isotopic peaks to be resolved. The third algorithm compensates for saturation of the instruments detector thereby recovering lost dynamic range, and lastly, the fourth algorithm increases confidence of peptide identifications by simplification of the fragment spectra. These algorithms are effective in processing raw data to detect features and extracting the attributes required for peptide identification, and make an important contribution to an analytical pipeline by detecting features that are higher quality and better segmented from other peptides in close proximity. The software has been developed in Python using Numpy and Pandas and made freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). Data are available via ProteomeXchange with identifier PXD030706. 2 Author SummaryThe primary goal of mass spectrometry data processing pipelines in the proteomic analysis of complex biological samples is to identify peptides accurately and comprehensively with abundance across a broad dynamic range. It has been reported that detection of low-abundance peptides for early-disease biomarkers in complex fluids is limited by the sensitivity of biomarker discovery platforms (1), the dynamic range of plasma abundance, which can exceed ten orders of magnitude (2), and the fact that lower abundance proteins provide the most insight in disease processes (3). As mass spectrometry hardware improves, the corresponding increase in amounts of data for analysis pushes legacy software analysis methods out of their designed specification. Additionally, experimentation with new algorithms to analyse raw data produced by instruments such as the Bruker timsTOF Pro has been hampered by the paucity of modular, open-source software pipelines written in languages accessible by the large community of data scientists. Here we present several algorithms for simplifying MS1 and MS2 spectra that are written in Python. We show that these algorithms are effective to help improve the quality and accuracy of peptide identifications.

bioinformatics↗

bioRxiv2021

Oligomerization-driven MLKL ubiquitylation antagonises necroptosis

Mixed lineage kinase domain-like (MLKL) is the executioner in the caspase-independent form of programmed cell death called necroptosis. Receptor Interacting serine/threonine Protein Kinase 3 (RIPK3) phosphorylates MLKL, triggering MLKL oligomerization, membrane translocation and membrane disruption. MLKL also undergoes ubiquitylation during necroptosis, yet neither the mechanism nor significance of this event have been demonstrated. Here we show that necroptosis-specific, multi-mono-ubiquitylation of MLKL occurs following its activation and oligomerization. Ubiquitylated MLKL accumulates in a digitonin insoluble cell fraction comprising plasma/organellar membranes and protein aggregates. This ubiquitylated form is diminished by a plasma membrane located deubiquitylating enzyme. MLKL is ubiquitylated on at least 4 separate lysine residues once oligomerized, and this correlates with proteasome- and lysosome-dependent turnover. Using a MLKL-DUB fusion strategy, we show that constitutive removal of ubiquitin from MLKL licences MLKL auto-activity independent of necroptosis signalling in mouse and human cells. Therefore, besides its role in the kinetic regulation of MLKL-induced death following an exogenous necroptotic stimulus, ubiquitylation also contributes to the restraint of basal levels of activated MLKL to avoid errant cell death.

molecular biology↗

bioRxiv2021

Physiological Substrates and Ontogeny-Specific Expression of the Ubiquitin Ligases MARCH1 and MARCH8 in mice

MARCH1 and MARCH8 are ubiquitin ligases that control the expression and trafficking of critical immunoreceptors. Understanding of their function is hampered by three major knowledge gaps: (i) it is unclear which cell types utilize these ligases; (ii) their level of redundancy is unknown; and (iii) most of their putative substrates have been described in cell lines, often overexpressing MARCH1 or MARCH8, and it is unclear which substrates are regulated by either ligase in vivo. Here we address these questions by systematically analyzing the immune cell repertoire of MARCH1- or MARCH8-deficient mice, and applying unbiased proteomic profiling of the plasma membrane of primary cells to identify MARCH1 and MARCH8 substrates. Only CD86 and MHC II were unequivocally identified as immunoreceptors regulated by MARCH1 and MARCH8, but each ligase carried out its function in different tissues. MARCH1 regulated MHC II and CD86 in professional and "atypical" antigen presenting cells of hematopoietic origin, whereas MARCH8 only operated in non-hematopoietic cells. Our results reveal that the range of cells constitutively endowed with antigen-presentation capacity is wider than generally appreciated. They also establish MARCH1 and MARCH8 as specialized regulators of CD4+ T cell immunity in two ontogenically distinct cellular compartments.

immunology↗

bioRxiv2021

The inflammasome-activated cytokine IL-1 is targeted for ubiquitylation and proteasomal degradation to limit its inflammatory potential

Interleukin-1{beta} (IL-1{beta}) is activated by inflammasome-associated caspase-1 in rare autoinflammatory conditions and in wide-spread diseases. Therefore, IL-1{beta} activity must be fine-tuned to enable antimicrobial responses whilst limiting collateral damage. Here we report that precursor IL-1{beta} is rapidly turned over by the proteasome and this correlates with its decoration by K11-, K63- and K48-linked ubiquitin chains. The ubiquitylation of IL-1{beta} is not just a degradation signal triggered by inflammasome priming and activating stimuli, but also limits IL-1{beta} cleavage by caspase-1. We further demonstrate that IL-1{beta} K133 is modified by ubiquitin and forms a salt bridge with IL-1{beta} D129. Loss of IL-1{beta} K133 ubiquitylation, or disruption of the K133:D129 electrostatic interaction, stabilizes IL-1{beta}. Accordingly, IL-1{beta}K133R/K133R mice display increased precursor IL-1{beta} upon inflammasome priming and increased bioactive IL-1{beta}, both in vitro and following LPS injection in vivo. These findings reveal new mechanisms for limiting IL-1{beta} activity and safeguarding against damaging inflammation.

immunology↗

bioRxiv2021

Mass Dynamics 1.0: A streamlined, web-based environment for analyzing, sharing and integrating Label-Free Data.

Label Free Quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web based analysis environment that can analyze and visualize LFQ data produced by software such as Maxquant. Unlike other tools, MD 1.0 utilizes cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardisation in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/. Contactwebb@wehi.edu.au

bioinformatics↗

bioRxiv2021

Tankyrase-mediated ADP-ribosylation is a novel regulator of TNF-induced death

Tumor necrosis factor (TNF) is an inflammatory cytokine that, upon binding to its receptor TNFR1, can drive cytokine production, cell survival, or cell death and is a major component of an organisms anti-pathogen repetoire1,2. TNF stimulation leads to the formation of two distinct signalling complexes, a well-defined membrane bound complex (complex 1), and a less well characterised cytosolic death inducing complex (complex 2). Using mass spectrometry, we identified the ADP-ribosyltransferase, tankyrase-1 (TNKS1/TNKS/ARTD5/PARP5a) as a novel native complex 2 component. Following a TNF-induced death stimulus TNKS1 is recruited to complex 2, resulting in complex 2 poly(ADP-ribosyl)ation (PARylation). Tankyrase inhibitors sensitise cells to TNF-induced death, which is correlated with increased complex 2 assembly. Tankyrase-mediated PARylation promotes recruitment of the E3 ligase RNF146 and RNF146 deficiency or proteasome inhibition results in increased levels of complex 2, suggesting that RNF146 causes proteasomal degradation of complex 2. Several viruses express ADP-ribose binding macrodomain proteins, and expression of the SARS-CoV-2 or VEEV macrodomain markedly sensitises cells to TNF-induced death. This suggests that ADP-ribosylation serves as yet another mechanism to detect pathogenic interference of TNF signalling and retaliate with an inflammatory cell death.

cell biology↗

bioRxiv2020

MSImpute: Imputation of label-free mass spectrometry peptides by low-rank approximation

Recent developments in mass spectrometry (MS) instruments and data acquisition modes have aided multiplexed, fast, reproducible and quantitative analysis of proteome profiles, yet missing values remain a formidable challenge for proteomics data analysis. The stochastic nature of sampling in Data Dependent Acquisition (DDA), suboptimal preprocessing of Data Independent Acquisition (DIA) runs and dynamic range limitation of MS instruments impedes the reproducibility and accuracy of peptide quantification and can introduce systematic patterns of missingness that impact downstream analyses. Thus, imputation of missing values becomes an important element of data analysis. We introduce msImpute, an imputation method based on low-rank approximation, and compare it to six alternative imputation methods using public DDA and DIA datasets. We evaluate the performance of methods by determining the error of imputed values and accuracy of detection of differential expression. We also measure the post-imputation preservation of structures in the data at different levels of granularity. We develop a visual diagnostic to determine the nature of missingness in datasets based on peptides with high biological dropout rate and introduce a method to identify such peptides. Our findings demonstrate that msImpute performs well when data are missing at random and highlights the importance of prior knowledge about nature of missing values in a dataset when selecting an imputation technique.

bioinformatics↗

bioRxiv2020

A regulatory interface on RIPK2 is required for XIAP binding and NOD signaling activity

Signaling via the intracellular pathogen receptors Nucleotide-binding oligomerization domain-containing proteins NOD1 and NOD2 requires Receptor Interacting Kinase 2 (RIPK2), an adaptor kinase that can be targeted for the treatment of various inflammatory diseases. However, the molecular mechanisms of how RIPK2 contributes to NOD signaling are not completely understood. We generated FLAG-tagged RIPK2 knock-in mice using CRISPR/Cas9 technology to study NOD signaling mechanisms at the endogenous level. Using cells from these mice we were able to generate a detailed map of post-translational modifications on RIPK2 during NOD signaling and we identified a new regulatory interface on RIPK2, which dictates the crucial interaction with the E3 ligase XIAP.

cell biology↗

bioRxiv2020

Phosphorylation by Aurora B kinase regulates caspase-2 activity and function

Mitotic catastrophe (MC) is an important oncosuppressive mechanism that serves to eliminate cells that become polyploid or aneuploidy due to aberrant mitosis. Previous studies have demonstrated that the activation and catalytic function of caspase-2 are key steps in MC to trigger apoptosis and/or cell cycle arrest of mitotically defective cells. However, the molecular mechanisms that regulate caspase-2 activation and its function are unclear. Here we identify six new phosphorylation sites in caspase-2 and show that a key mitotic kinase, Aurora B kinase (AURKB), phosphorylates caspase-2 at the highly conserved residue S384. We demonstrate that phosphorylation at S384 blocks caspase-2 catalytic activity and apoptosis function in response to mitotic insults, without affecting caspase-2 dimerisation. Moreover, molecular modelling suggests that phosphorylation at S384 may affect substrate binding by caspase-2. We propose that caspase-2 S384 phosphorylation by AURKB is a key mechanism that controls caspase-2 activation during mitosis.

cell biology↗

bioRxiv2019

Simplified high-throughput methods for deep proteome analysis on the timsTOF Pro

Recent advances in mass spectrometry technology have seen remarkable increases in proteomic sequencing speed, while improvements to dynamic range have remained limited. An exemplar of this is the new timsTOF Pro instrument, which thanks to its trapped ion mobility, pushes effective fragmentation rates beyond 100Hz and provides accurate CCS values as well as impressive sensitivity. Established data dependent methodologies underutilize these advances by relying on long analytical columns and extended LC gradients to achieve comprehensive proteome coverage from biological samples. Here we describe the implementation of methods for short packed emitter columns that fully utilize instrument speed and CCS values by combining rapid generation of deep peptide libraries with enhanced matching of single shot data dependent sample analysis. Impressively, with only a 17 minute separation gradient (50 samples per day), the combination of high performance chromatography and CCS enhanced library based matching resulted in an average of 5,931 protein identifications within individual samples, and 7,244 proteins cumulatively across replicates from HeLa cell tryptic digests. Additionally, an ultra-high throughput setup utilizing 5 min gradients (180 samples per day) yielded an average of 3,666 protein identifications within individual samples and 4,659 proteins cumulatively across replicates. These workflows are simple to implement on available technology and do not require complex software solutions or custom-made consumables to achieve high throughput and deep proteome analysis from biological samples.

biochemistry↗

bioRxiv2018

Therapeutic blockade of Activin-A improves NK cell function and anti-tumor immunity

Natural killer (NK) cells are innate lymphocytes that play a major role in immunosurveillance against tumor initiation and metastasis spread. Signals and checkpoints that regulate NK cell fitness and function in the tumor microenvironment are not well defined. Transforming grow factor (TGF)-{beta} is a recognized suppressor of NK cells that inhibits IL-15 dependent signaling events and induces cellular transdifferentiation, however the role of other SMAD signaling pathways in NK cells is unknown. In this report, we show that NK cells express the type I Activin receptor, ALK4, which upon binding its ligand Activin-A, phosphorylates SMAD2/3 to efficiently suppress IL-15-mediated NK cell metabolism. Activin-A impairs human and mouse NK cell proliferation and downregulates intracellular granzyme B levels to impair tumor killing. Similar to TGF-{beta}, Activin-A also induced SMAD2/3 phosphorylation and drove NK cells to upregulate several ILC1-like surface markers including CD69, TRAIL and CD49a. Activin-A also induced these changes on TGF-{beta} receptor deficient NK cells, highlighting that Activin-A and TGF-{beta} are independent pathways that drive SMAD2/3-mediated NK cell suppression. Finally, therapeutic inhibition of Activin-A by Follistatin significantly slowed orthotopic melanoma growth in mice. These data highlight independent SMAD2/3 pathways target NK cell fitness and function and identify a novel therapeutic axis to promote tumor immunity.\n\nOne Sentence Summary: Activin-A can directly inhibit NK cell effector functions, promote NK cells transdifferentiation into ILC1-like cells and suppress anti-melanoma immunity.

immunology↗

bioRxiv2018

MSCypher: an integrated database searching and machine learning workflow for multiplexed proteomics.

Improvements in shotgun proteomics approaches are hampered by increases in multiplexed (chimeric) spectra, as improvements in peak capacity, sensitivity or dynamic range all increase the number of co-eluting peptides. This results in diminishing returns using traditional search algorithms, as co-fragmented spectra are known to decrease identification rates. Here we describe MSCypher, a freely available software suite that enables an extensible workflow including a hybrid supervised machine learned strategy that dynamically adjusts to individual datasets. This results in improved identification rates and quantification of low-abundant peptides and proteins. In addition, the integration of peptide de novo sequencing and database searching enables an unbiased view of variants and high-intensity unassigned peptide spectral matches.\n\nHighlightsO_LIOpen-source end-to-end label-free proteomics workflow\nC_LIO_LIIntegrated database searching and machine learning\nC_LIO_LICustomisable and extensible workflow including de novo sequencing\nC_LIO_LIOptimised for multiplexed spectra, challenging proteomics datasets and peptidomics applications\nC_LI

bioinformatics↗

bioRxiv2018

Aspartyl Protease 5 matures virulence factors found at the host-parasite interface in Toxoplasma gondii

Toxoplasma gondii infects approximately 30% of the worlds population, causing disease primarily during pregnancy and in individuals with weakened immune systems. Toxoplasma secretes and exports effector proteins that modulate the host during infection and several of these proteins are processed by the Golgi-associated Aspartyl Protease 5 (ASP5). Here, we identify ASP5 substrates by selectively enriching N-terminally-derived peptides from wildtype and {Delta}asp5 parasites. We reveal over two thousand unique Toxoplasma N-terminal peptides, mapping to both natural N-termini and protease cleavage sites. Several of these peptides mapped directly downstream of the characterised ASP5-cleavage site, arginine-arginine-leucine (RRL). We validate candidates as true ASP5 substrates, revealing they are not processed in parasites lacking ASP5, nor in wild type parasites following mutation of the motif from RRL{longrightarrow}ARL. All new ASP5 substrates are dense granule proteins, and interestingly none appear to be exported, thus differing from the analogous system in related Plasmodium spp., instead revealing that the majority of substrates reside within the parasitophorous vacuole (PV), and its membrane (the PVM), including two kinases and one phosphatase. Furthermore, we show that several of these ASP5-substrates are virulence factors, with their removal leading to attenuation in a mouse model, suggesting that phosphorylation at the host-parasite interface is important for virulence. Collectively, these data constitute the first in-depth analyses of the total list of ASP5 substrates, and shed new light on the role of ASP5 as a maturase of dense granule proteins during the Toxoplasma lytic cycle.

microbiology↗

bioRxiv2018

Protein Kinase A Negatively Regulates Ca2+ signalling in Toxoplasma gondii

The phylum Apicomplexa comprises a group of obligate intracellular parasites that alternate between intracellular replicating forms and actively motile extracellular forms that move through tissue. Parasite cytosolic Ca2+ signalling activates motility, but how this is switched off after invasion is not understood. Here we show that the cAMP-dependent Protein Kinase A catalytic subunit 1 (PKAc1) of Toxoplasma is responsible for suppression of Ca2+ signalling upon host cell invasion. We demonstrate that that PKAc1 is sequestered to the parasite periphery by dual acylation of its regulatory subunit PKAr1. Newly invaded PKAc1-deficient parasites exit host cells shortly thereafter in a perforin-like protein 1 (PLP-1)-dependent fashion. We demonstrate that loss of PKAc1 results in an inability to rapidly downregulate cytosolic Ca2+ levels shortly after invasion. Furthermore, we demonstrate that PKAc1 also specifically negatively regulates resting cytosolic Ca2+ in conditions that mimic intracellularity. We also show that cAMP and cGMP have opposing role in microneme secretion, further supporting evidence that cAMP signalling has a suppressive role during motility. Together, this work provides a new paradigm in understanding how Toxoplasma and related apicomplexan parasites regulate infectivity.

microbiology↗