A Bayesian mixture modelling approach for spatial proteomics

Crook, Oliver M.; Mulvey, Claire M.; Kirk, Paul; Lilley, Kathryn S.; Gatto, Laurent

doi:10.1371/journal.pcbi.1006516

Cited by 55 publications

(84 citation statements)

References 67 publications

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“…The 62 newly validated proteins were added to the previous 656 markers to give 718 markers defining 26 distinct subcellular niches. We analyzed the data by a recently developed Bayesian classification method based on t-augmented Gaussian mixture models (TAGM) to probabilistically assign proteins to a set of defined classes (Crook et al, 2018. This method performs similarly to the established modelling methods (e.g.…”

Section: Figure 2 Validation Of Hyperlopit-predicted Subcellular Locmentioning

confidence: 99%

A subcellular atlas ofToxoplasmareveals the functional context of the proteome

Barylyuk

Kořený

et al. 2020

Preprint

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Apicomplexan parasites cause major human disease and food insecurity. They owe their considerable success to novel, highly specialized cell compartments and structures. These adaptations drive their recognition and nondestructive penetration of host's cells and the elaborate reengineering of these cells to promote growth, dissemination, and the countering of host defenses. The evolution of unique apicomplexan cellular compartments is concomitant with vast proteomic novelty that defines these new cell organizations and their functions. Consequently, half of apicomplexan proteins are unique and uncharacterized, and these cells are, therefore, very poorly understood. Here, we determine the steadystate subcellular location of thousands of proteins simultaneously within the globally prevalent apicomplexan parasite Toxoplasma gondii. This provides unprecedented comprehensive molecular definition to these cells and their novel compartments, and these data reveal the spatial organizations of protein expression and function, adaptation to hosts, and the underlying evolutionary trajectories of these pathogens.

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Section: Figure 2 Validation Of Hyperlopit-predicted Subcellular Locmentioning

confidence: 99%

A subcellular atlas ofToxoplasmareveals the functional context of the proteome

Barylyuk

Kořený

et al. 2020

Preprint

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“…A prediction of the subcellular localisation for each protein was then made by matching to profiles of known organelle markers using supervised machine learning through the support vector machines (SVM), and using Bayesian statistical modelling through the T-Augmented Gaussian Mixture model (TAGM) method ( Fig. 1D) (3). The latter takes into account the uncertainty that arises when classifying proteins that reside in multiple locations, or unknown functional compartments and also those that dynamically move within the cell, providing a richer overall analysis of our spatial proteomics data (3).…”

Section: Lopit-dc On Mitochondrial Relocated Golgin-97 and Gcc88mentioning

confidence: 99%

“…1D) (3). The latter takes into account the uncertainty that arises when classifying proteins that reside in multiple locations, or unknown functional compartments and also those that dynamically move within the cell, providing a richer overall analysis of our spatial proteomics data (3).…”

Section: Lopit-dc On Mitochondrial Relocated Golgin-97 and Gcc88mentioning

confidence: 99%

“…Posterior localisation probabilities. The posterior probability that a protein belongs to a subcellular niche, henceforth referred to as the posterior localisation probability, is computed using the TAGM-MAP method (3). Briefly, the parameters of the T-augmented Gaussian mixture model are learnt by maximising the log posterior of the parameters with respect to the data, to obtain maximum a posteriori (MAP) estimates of the parameters.…”

Section: Correlative Fm and Electron Tomography Of Resin-embedded Cellsmentioning

confidence: 99%

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Determining the content of vesicles captured by golgin tethers using LOPIT-DC

Shin

Crook²,

Borgeaud

et al. 2019

Preprint

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The internal organisation of the cell depends on tethers at destination organelles to selectively capture incoming transport vesicles to facilitate SNARE-mediated fusion. The golgin long coiledcoil proteins function as tethers that contributes to this specificity at the Golgi (1). Golgin-97, golgin-245 and GCC88 golgins of the trans-Golgi capture vesicles derived from endosomes, which serve to recycle the critical Golgi machinery required to deliver lysosomal hydrolases and to maintain exocytosis. Retrograde trafficking from endosomes to the trans-Golgi network (TGN) is a complex process that involves the sorting of transmembrane cargo proteins into distinct transport vesicles by adaptors from multiple pathways. The content of these distinct vesicles, which golgin they target and the factors that mediate this targeting are not well understood. The major challenges that have limited advances in these areas is the transient nature of vesicle tethering, and the redundancies in their mechanisms that confound experimental dissection. To gain better insight into these problems, we performed organelle proteomics using the Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) method on a system in which an ectopic golgin causes vesicles to accumulate in a tethered state (2). By incorporating Bayesian statistical modelling into our analysis (3), we determined that over 45 transmembrane proteins and 51 peripheral membrane proteins of the endosomal network are on vesicles captured by golgin-97, including known cargo and components of the clathrin/AP-1, retromer-dependent and -independent transport pathways. We also determined a distinct class of vesicles shared by golgin-97, golgin-245 and GCC88 that is enriched in TMEM87A, a multi-pass transmembrane protein of unknown function that has previously been implicated in endosome-to-Golgi retrograde transport (4). Finally, we categorically demonstrate that the vesicles that these golgins capture are retrograde transport vesicles based on the lack of enrichment of lysosomal hydrolases in our LOPIT-DC data, and from correlative light electron tomography images of spherical vesicles captured by golgin-97. Together, our study demonstrates the power of combining LOPIT-DC with Bayesian statistical analysis in interrogating the dynamic spatial movement of proteins in transport vesicles.

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“…Following Crook et al [21], we assume ratio estimates for variants in the junk cluster follow a generalized t-distribution with degrees of freedom ν = 4, mean µ taken as the sample mean of all the ratio estimates (µ = J j=1θ j /J), and scale parameter ψ taken as…”

Section: Mixture Modelmentioning

confidence: 99%

MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates

Kirk

Burgess

2019

Preprint

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Motivation: Mendelian randomization is an epidemiological technique that uses genetic variants as instrumental variables to estimate the causal effect of a risk factor on an outcome.We consider a scenario in which causal estimates based on each variant in turn differ more strongly than expected by chance alone, but the variants can be divided into distinct clusters, such that all variants in the cluster have similar causal estimates. This scenario is likely to occur when there are several distinct causal mechanisms by which a risk factor influences an outcome with different magnitudes of causal effect. We have developed an algorithm MR-Clust that finds such clusters of variants, and so can identify variants that reflect distinct causal mechanisms. Two features of our clustering algorithm are that it accounts for uncertainty in the causal estimates, and it includes 'null' and 'junk' clusters, to provide protection against the detection of spurious clusters.Results: Our algorithm correctly detected the number of clusters in a simulation analysis, outperforming the popular Mclust method. In an applied example considering the effect of blood pressure on coronary artery disease risk, the method detected four clusters of genetic variants. A hypothesis-free search suggested that variants in the cluster with a negative effect of blood pressure on coronary artery disease risk were more strongly related to trunk fat percentage and other adiposity measures than variants not in this cluster.Availability and Implementation: MR-Clust can be downloaded from https://

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A Bayesian mixture modelling approach for spatial proteomics

Cited by 55 publications

References 67 publications

A subcellular atlas ofToxoplasmareveals the functional context of the proteome

A subcellular atlas ofToxoplasmareveals the functional context of the proteome

Determining the content of vesicles captured by golgin tethers using LOPIT-DC

MR-Clust: Clustering of genetic variants in Mendelian randomization with similar causal estimates

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