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A subcellular atlas of Toxoplasma reveals the functional context of the proteome

Konstantin Barylyuk, Ludek Koreny, Huiling Ke, Simon Butterworth, Oliver M. Crook, Imen Lassadi, Vipul Gupta, Eelco Tromer, Tobias Mourier, Tim J. Stevens, Lisa M. Breckels, Arnab Pain, Kathryn S. Lilley, Ross F. Waller

Preprint posted on 23 April 2020 https://www.biorxiv.org/content/10.1101/2020.04.23.057125v1

hyperLOPIT: mapping the Toxoplasma sub cellular proteome atlas

Selected by Mariana De Niz

Background

Apicomplexa is a phylum of highly adapted unicellular eukaryotes specialized for intracellular parasitism. They owe their success to highly specialized cell compartments and structures, examples of which include the apical complex, and gliding motility structures. These adaptations allow the recognition and non-destructive invasion of a variety of host cells, and the elaborate reengineering of these cells to promote growth, dissemination, and the countering of host defenses. Upon invasion of these host cells, the parasites form, and typically remain within, a ‘parasitophorous vacuole’ decorated with secreted parasite proteins (1). Furthermore, parasite-secreted proteins also modify existing host cell compartments and organelles, thus interfering with host control of defense and metabolism, changing the mechanical properties of the host cell, and altering how cells interact with other cells in their environment.

The novelty and divergence of apicomplexan cell compartments is concomitant with tremendous novelty of genes and proteins. Moreover, each apicomplexan lineage possesses its own new proteins. The complete proteomes of most parasite compartments remain very poorly known. Here, Barylyuk and colleagues use the spatial proteomic method hyperLOPIT to determine the subcellular location of thousands of proteins simultaneously within Toxoplasma gondii (2).

Figure 1. hyperLOPIT reveals organelle protein ensembles through measuring co-fractionation profiles of proteins. (Left) Schematic of T. gondii tachyzoite showing the main subcellular compartments and structures and their major functional roles. (Right) Mapping of subcellular marker proteins on the t-SNE projection of T. gondii spatial proteome data. (Used from Figure 1, Ref 2).

 

 

Key findings and developments

The authors adapted the hyperLOPIT method for whole-cell spatial proteomics to Toxoplasma gondii tachyzoites, the extracellular parasite form primed for host cell invasion. The hyperLOPIT method exploits distinct abundance distribution profiles that organelles and subcellular structures form upon biochemical fractionation.

The protein fractionation data were analyzed for common abundance distribution patterns as evidence of protein association within subcellular niches.Machine learning was used to visualize the 30-dimensional data. The clusters were found to represent all known apicomplexan compartments including membranous organelles (mitochondrion, micronemes, ER, Golgi), cytoskeletal elements (inner membrane complex, apical complex structures) and subcompartments (outer and inner peripheral, and integral plasma membrane proteins). Altogether, a highly resolved proteomic map of the T. gondii tachyzoite was obtained. For validation of the hyperLOPIT clusters, 62 previously uncharacterized proteins associated with the clusters representing different organelles or sub-compartments, were epitope-tagged, and the location determined by immuno-fluorescence microscopy. HyperLOPIT succeeded in assigning thousands of unknown proteins to subcellular niches.

Steady-state determination of protein locations in a population of cells overlooks the dynamic behaviors that many proteins can have, including regulated location changes, trafficking intermediates, organelle contact points, and proteins with dual or multiple locations. The authors tested whether these dynamic protein behaviours can be detected and showed enrichment of some proteins with a dynamic nature across multiple compartments, while others known to be static, have single assignments. This suggests the method enables prediction of proteins with a dynamic localization as well, however, the authors point towards the limitations to predictability and fractionation methods used.

HyperLOPIT was altogether found to allow distinction of profiles of proteins associated either directly or indirectly with membranous components of organelles, and organelle soluble proteins. Resolution allowing sub-compartmental and multi-compartment divisions was achieved – and the authors discuss relevant findings for the IMC, the plasma membrane proteome, the ER, and the apicoplast proteomes, as well as cytosolic large protein complexes (e.g. proteasome and ribosome subunits).

HyperLOPIT allowed assigning ‘hypothetical proteins’ to known compartments, representing a significant advancement in our knowledge of protein composition of subcellular compartments and niches in Toxoplasma. Significant findings included separation of rhoptries into two distinct clusters, one of which is enriched in soluble cargo, and the other enriched in proteins associated with organelle maintenance and biogenesis; and a significant expansion of our knowledge of the proteome forming the plasma membrane – the interface between host and parasite.

Moreover, the authors show that HyperLOPIT resolves the cellular landscapes of proteome expression, function, adaption and evolution within the parasite. The four key findings on this respect explored in this work are:

  1. That in the past, an objective assessment of the coordination of gene expression for compartment proteins had not been previously possible due to a lack of enough knowledge on the spatial distribution of the proteome. Here, the authors identified several clusters with tight transcriptional regulatory control, while other compartments showed small or no evidence of coordinated expression.
  2. It is expected that proteomic data from subcellular niches can offer information on the biochemical conditions of these microenvironments. The authors evaluated information regarding pH differences, composition of signal peptides from different endomembrane niches, and distribution of lengths of membrane-spanning proteins across different compartments which are likely to reflect the lipid composition across the cell, and may govern protein-sorting events. The authors also compared this information with results from the genome-wide CRISPR screen (3), giving further insight into the different localizations of dispensable and indispensable proteins.
  3. Analysis of single nucleotide polymorphism properties (i.e. ratio of rates of non-synonymous to synonymous point mutations for a gene) can give information on the strength and nature of selective pressures on a protein. The authors identified compartments with highly positively skewed distribution implying positive selection for change, and high tolerance for changes, in the external plasma membrane, soluble content of rhoptries, and dense granules. This is not unexpected, as proteins in these compartments are at the frontline of host-pathogen interaction and adaptation. An unexpected mutation behavior, however, was observed with the mitochondrial soluble proteins, and the apicoplast, which the authors discuss as important for metabolic control, possibly contributing to tissue/taxon preference or even virulence.
  4. The authors were able to explore the question ‘when in the evolution of these parasites did different cell compartments and functions display the greatest rates of innovation?’. They found that different cell compartments display different rates of evolutionary protein innovation. At the most ancient level, were compartments related to cytosol and complexes for protein expression, sorting and turnover. Conversely, compartments most enriched for recent orthologues included dense granules (displaying the greatest novelty), rhoptry soluble fraction, micronemes, conoid and peripheral surface proteins. Collectively, these data provide an unprecedented view of the evolutionary chronology of apicomplexan cells and their trajectory to parasitism.

 

What I like about this preprint

I like this preprint because it bridges an enormous gap in our knowledge of parasitology- specifically the proteome’s sub-compartment atlas of Toxoplasma gondii. I think it’s innovative in its methods, and it addresses important questions in the biology of parasitism, which can be now a) further explored in detail in T. gondii, and b) translated to other parasites. I think this is interesting work not only for its relevance of these parasites to human and veterinary health, but also from a cell biology point of view, it opens an exciting window to understand the proteome, including in a dynamic manner. I expect this work will have a lot of influence in parasitology in the near future.

 

References

  1. Cesbron-Delauw MF, et al, (2008) Apicomplexa in mammalian cells: trafficking to the parasitophorous vacuole, Traffic, 9(5).
  2. Barylyuk K, et al (2020) A subcellular atlas of Toxoplasma reveals the functional context of the proteome, bioRxiv.
  3. Sidik SM, et al. (2016) A genome-wide CRISPR screen in Toxoplasma identifies essential apicomplexan genes, Cell, 166.
  4. Geladaki, A., Kočevar Britovšek, N., Breckels, L.M., Smith, T.S., Vennard, O.L., Mulvey, C.M., Crook, O.M., Gatto, L., and Lilley, K.S. (2019) Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat. Commun. 10, 331.
  5. Crook, OM et al. (2020) A semi-supervised Bayesian approach for simultaneous protein sub-cellular localisation assignment and novelty detection, bioRxiv.
  6. Jacot D et al (2016) Apicomplexan energy metabolism: carbon source promiscuity and the quiescence hyperbole, Trends in Parasitology. 32(1).

 

Acknowledgement

I am very grateful for Ross Waller and Konstantin Barylyuk for their exciting discussion and involvement in this highlight.

 

Posted on: 16 June 2020

doi: https://doi.org/10.1242/prelights.21962

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Author's response

Konstantin Barylyuk and Ross Waller shared

Open questions 

1.Starting on a more general note, this is an exciting piece of work for the entire parasitology field. I imagine its relevance to other parasites has enormous potential. What are key limitations you envisage in trying to translate the application of the methodology you used, to other intracellular parasites such as Plasmodium, and to extracellular parasites such as Trypanosoma brucei?

Indeed, the hyperLOPIT approach has a great potential for characterising spatial proteomes of other parasitic protists. In fact, our research group is currently applying the method to a range of new organisms, including Plasmodium falciparum and several species of marine protists from lineages closely related to Apicomplexa that engage in a spectrum of symbiotic relationships with animals. The preliminary results indicate that hyperLOPIT is equally powerful in these cases. Furthermore, we are aware that several projects on spatial proteome mapping by hyperLOPIT are underway in other research groups, including the work of our colleagues from the Department of Biochemistry in Cambridge on African trypanosomes. Again, the coverage of the proteome, the resolution, and the level of detail achieved by hyperLOPIT in these cases is truly spectacular. We are convinced that the method has broad applicability in protists.

The main limitation of hyperLOPIT is the requirement of a large amount of starting material. This restricts the method’s applicability to organisms that are available in reasonably scalable cell culture or can be obtained in large quantities from natural sources. Another limitation is the steady-state nature of the spatial proteome map generated by hyperLOPIT. When the input population of cells is highly heterogeneous, which is frequently the case as it is often difficult to tightly synchronise a culture, the resolution of the spatial proteome map may be significantly reduced due to averaging over a large number of distinct cells captured in different stages of the cell cycle or even organism’s life cycle. Of course, learning the biology of the target organism and developing methods for maintaining and controlling it in the culture allows one to circumvent the limitations of hyperLOPIT mentioned above. However, it is not always possible. Hence, further development of the hyperLOPIT technique to improve its sensitivity, both the spatial and temporal resolution, and throughput will enable spatial proteome mapping in less accessible systems. And, indeed, such efforts are constantly underway in the laboratory of Prof Kathryn Lilley here in Cambridge (e.g. 3).

2.Also on a more general note, how easily can you explore the proteome of the parasite throughout each and every stage of its life cycle, particularly given that many of them show sometimes asynchronous development – for instance the jump between one Plasmodium stage and the next (eg. ring and trophozoite) is not a discrete step, but a continuum, assumingly with important changes occurring in the transition?

As mentioned above, hyperLOPIT requires a significant amount of starting material and produces a steady-state, population-averaged map of protein subcellular locations. The protocol is also relatively complex, time-consuming, and labour-intense. All these factors limit hyperLOPIT’s suitability to fast dynamic processes in asynchronous populations of cells. Typically, only a small fraction of cells undergo a rapid and dynamic developmental transition at any given moment, whereas the majority of the population persists in one or several differentiated states. A snapshot of such a mixed population taken by hyperLOPIT will be an average of all the cells, dominated by the signal from the most abundant cell type. The Bayesian machine-learning method used in our study to assign the unknown proteins to subcellular niches can handle this problem by quantifying the uncertainty of classification – but only to a certain extent. So, capturing these dynamic processes such as cell differentiation will be largely limited to the level of synchronicity of the cell population that can be achieved. Having said that, optimising hyperLOPIT to apply it to dynamic processes is currently an area of intense research. Stay tuned!

3.Do you think your method will allow enough resolution to study and capture differences during phenomena such as commitment to latent stages for example, in Toxoplasma and Plasmodium?

We are optimistic that applying hyperLOPIT to study the differentiation of apicomplexan parasites into dormant, persistent stages, such as the Toxoplasma bradyzoite, will provide extremely useful new data and insight into this process. In Toxoplasma, an in vitro model for bradyzoite differentiation is accessible, and the timeframe of events potentially allows taking a few snapshots of the spatial proteome during this process, even though the limitations of hyperLOPIT mentioned above will have to be carefully considered for interpreting the observations. Olly Crook, whose expertise in biological statistics and machine-learning methods have been key to successful analysis of the data reported in our paper, has been developing new powerful and flexible machine-learning algorithms for spatial proteomics (5). These new algorithms will enable the discovery of new, previously uncharacterized subcellular niches and detection of proteins that are differentially localized between the spatial proteomes of two developmental states. Both advances are extremely important in the context of investigating dynamic processes. We will be very excited to analyse the dynamic changes of the spatial proteome of Toxoplasma upon differentiation into bradyzoites – it is definitely on our to-do list.

4.Having focused in an intracellular parasite, and having explored in higher detail the cluster related to plasma membrane, in the future, can you simultaneously study the host and the parasite proteome to see the changes occurring during parasitism on both? More work across various cell biology fields has focused on understanding the relevance of membrane contact sites, and the parasitophorous vacuole membrane seems an exciting prospect.

Toxoplasma is propagated in culture in the so-called lytic cycle that replicates the acute infection in human and animal tissues. During this cycle, the parasite alternates between the two major states: the extracellular tachyzoite that is committed to invasion, and the intracellular tachyzoite that grows and replicates inside the host cell. For the first application of hyperLOPIT, the fully differentiated extracellular tachyzoite that is arrested in the cell cycle was a more practical choice. Naturally, the intracellular stage is the next logical target. The hyperLOPIT approach was originally developed using a mammalian cell model rendering the analysis of Toxoplasma-infected human cells fairly straightforward. In our ongoing work, we are aiming to address questions on both the parasite and the host cell sides. What are the differences between the spatial proteomes of extra- and intracellular tachyzoites? What are the responses of the host cell’s spatial proteome to the infection? What are the repertoire and the subcellular destinations of the proteins secreted by Toxoplasma in the host cell? Since both the parasite and the host cells are present in the sample, in this case, the main challenge is how to differentiate the two. Luckily, the protein sequences of the two species are different enough to distinguish them in the mixture, and the conditions of the mammalian cell lysis are a lot milder than what is required for breaking Toxoplasma cells open. The parasitophorous vacuole membrane, as a major interface between host and parasite, and already the known destination of many Toxoplasma secreted proteins, is indeed an exciting compartment that we hope to capture. As mentioned above, we will soon have access to new machine-learning methods tailored specifically to address challenges emerging in the analysis of such complex data.

5.A lot of the work in parasitology has assumed good translation between parasites- namely in the form of orthologues/homologues, to guide exploration of proteins and genes. Throughout your work, did you find any surprises on this respect, of proteins previously assumed to be elsewhere or previously misclassified?

Parasites evolve under the constant pressure from the host’s defence mechanisms. Contrary to the common point of view that the reduction of complexity is the major theme in their evolution, parasites gain a lot of novelty in the process of adaptation to their ever-changing and unwelcoming habitat. This rapid divergence limits the possibility to infer functions of unknown proteins by orthology to comparisons of closely related lineages. For example, according to our in-house orthology analysis, T. gondii and Plasmodium falciparum share only 2,712 orthologous groups between 8,322 and 5,303 protein-coding genes, respectively. Even in these cases, we are looking at the more conservative parts of the cell, such as the cytosol, the mitochondrion, the ER and Golgi, the IMC. In our paper, we discuss several examples of Toxoplasma proteins found in subcellular locations other than what would be expected based on the knowledge available from other organisms or inferred by orthology. One such example is the localisation of an apicomplexan-specific RNA-binding protein containing the so-called RAP domain in the apicoplast, whereas all the other members of this protein family are targeted to the mitochondrion. The functions of RAP-domain proteins remain elusive, and it is even more intriguing to find one of them in a different organelle. Other examples are dense-granule proteins originally annotated as belonging to rhoptries and differentiation of a subset of ER proteins including the canonical ER marker protein BiP from the rest of the ER. So, it is clear that while homology can be a useful guide to function, there are many processes driving proteins to new functions and behaviours and caution should be applied when making assumptions based on conservation.

6.Going to some specifics, I was wondering if you discuss in more detail the relevance of two of your findings: the first – that dense granules seem to have the highest novelty in proteins. The second: the unexpected mutation behaviour in mitochondrial soluble proteins and in the apicoplast, which you suggest could be a contributing factor for metabolic control, and even guide tropism, taxon preference, and even virulence across parasites and/or strains. For instance, for the latter, do you expect big changes in the proteome atlas based on the parasite’s tissue tropism?

As mentioned above, apicomplexan parasites are constantly challenged by the host’s defence mechanisms and must rapidly change to adapt to this evolutionary pressure. The secretory organelles and the parasite’s surface are most exposed to the host. Furthermore, Toxoplasma gondii exploits a remarkably wide range of intermediate hosts: virtually any warm-blooded animal can be infected. Toxoplasma must, therefore, arm itself with a broad arsenal of secretory effectors to enable successful invasion and survival within such a diversity of hosts. Hence is the accumulation of novel proteins in the secretory compartments of the parasite including the dense granules.

The mitochondrion and the apicoplast are different in this regard since they are never exposed to the host. These are endosymbiotic organelles that used to be free-living organisms in the distant past. They contain their own genomes, albeit heavily reduced, and maintain dedicated machinery for gene expression. Their main functions are associated with essential metabolic pathways, which can often be reduced in parasitic organisms to a bare minimum thanks to the availability of metabolites and nutrients from the host. Thus, we expected to find the proteomes of these organelles to be largely conserved and stable. Much to our surprise, we found a significantly higher than the average density of polymorphisms in the genes encoding the soluble mitochondrial and apicoplast proteins but otherwise neutral or only slightly elevated selection for changes. These observations suggest a particular relevance of codon usage to these compartments and their metabolism. The ability to modulate physiological states has been seen as relevant to other parasites also, such as Plasmodium which switches from anaerobic to aerobic metabolism (for review see 6). These switches have been suggested to enable the parasite to enter or exit from periods of dormancy or altered physiological states of their hosts. The population-level genetic data for Toxoplasma, mapped onto our hyperLOPIT data, suggest the hypothesis that tuning of metabolism might equally be relevant to Toxoplasma parasitism. Indeed, the Toxoplasma population data is drawn from isolates with differences in the host taxa and tissues that they favour, as well as virulence properties. However, rather than ‘big changes’ in the protein atlases, we’d anticipate these to represent subtle fine tuning and, if so, this points to further evidence of the exquisite adaptation of these successful parasites. This is an example of the exciting and intriguing hypotheses generated by the types of data that hyperLOPIT can achieve, and that will now need testing.

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