Five-layer systems analysis of Leishmania stage differentiation reveals an essential role for protein degradation in parasite development

During their infectious cycle, many protist and fungal pathogens undergo complex developmental transitions that adapt their biology to different host environments, thus ensuring transmission, persistence, or immune evasion. For example, stage differentiation of the malarial parasite Plasmodium spp. is tightly controlled at transcriptional levels by the dynamic interplay between epigenetic regulators that remodel the chromatin landscape to accommodate stage-specific transcription factors, such as AP2 DNA-binding family members (Hollin and Le Roch, 2020; Modrzynska et al., 2017). Likewise, the transition from fast-growing tachyzoites to slow-growing bradyzoites during mammalian Toxoplasma infection is regulated by the single master TF BFD1 (Waldman et al., 2020). Unlike these apicomplexan parasites, stage differentiation in the trypanosomatid pathogen Leishmania spp. is not regulated at the level of transcriptional control, raising the question of how these parasites establish and maintain the various adaptive forms that develop inside their insect and mammalian hosts.

Leishmania spp. are protist parasites of humans that represent a global public health problem causing a series of immunopathologies termed the leishmaniases (Alvar et al., 2012; WHO, 2023; Maia et al., 2023; Barbiero et al., 2024). These parasites differentiate into various developmental forms that are adapted for extracellular proliferation inside the sand fly midgut (procyclic promastigotes), transmission from the vector to the mammalian host during uptake of a blood meal (metacyclic promastigotes), and intracellular proliferation inside fully acidified macrophage phagolysosomes (amastigotes) (Handman, 1999). Unlike other eukaryotes, Leishmania development is not controlled transcriptionally as protein-coding genes in these parasites lack individual promoters and are constitutively transcribed at all stages involving parasite-specific processes such as polycistronic transcription and trans-splicing (Clayton, 2002; Liang et al., 2003). Despite the largely constitutive gene transcription, different Leishmania stages are characterized by striking morphological, biochemical, and metabolic changes (McConville and Ralton, 1997; McConville et al., 2007; Rosenzweig et al., 2008; McConville and Naderer, 2011; Sunter and Gull, 2017; Dandugudumula et al., 2022). These are regulated by a plethora of post-transcriptional mechanisms, including differential mRNA turnover through the binding of various proteins and protein complexes to the 5’ and 3’ untranslated regions (5’ UTRs and 3’ UTRs, respectively) (Trenaman et al., 2019; Boucher et al., 2002; McNicoll et al., 2005; Bringaud et al., 2007; Azizi et al., 2017; Clayton and Estevez, 2011), that can trigger mRNA degradation via deadenylation and decapping (Schwede et al., 2009; Kramer, 2017). Differential gene expression in Leishmania can further be regulated at the translational level, e.g., through (i) selective ribosome recruitment by one of the six isoforms of the Leishmania cap-binding protein eIF4E that bind to the 5’ UTR, (ii) stabilizing the poly(A) tail via the recruitment of Poly(A)-binding protein 1 (PABP1) to the 3’ UTR promoting translation, or (iii) the differential expression of ribosomal proteins that can increase the landscape of specialized ribosomes (Yoffe et al., 2004; Yoffe et al., 2006; David et al., 2010; Freire et al., 2017; Shrivastava et al., 2021; Assis et al., 2021; Piel et al., 2022; Rodríguez-Almonacid et al., 2023; Gutierrez Guarnizo et al., 2023; Rajan et al., 2024). Finally, differential gene expression in Leishmania can also be controlled at the level of protein stability, through stage-specific expression of proteasomal components, autophagy-related genes, or lysosomal proteases (Besteiro et al., 2006; Williams et al., 2006; Besteiro et al., 2007; Williams et al., 2012; Williams et al., 2013; Sakamoto et al., 2021; Casgrain et al., 2016).

While each level of gene expression control has been studied individually in different Leishmania species, how these different types of regulation are integrated during Leishmania stage differentiation remains to be elucidated. Here, we approached this open question by applying a five-layer, systems-level analysis on the two major life cycle stages of Leishmania donovani, i.e., hamster-isolated, bona fide amastigotes and derived promastigotes. Our analysis draws a complex picture of the Leishmania differentiation process that emerges from complex, co-regulated genetic networks involving mRNA turnover, protein translation, protein phosphorylation, and protein degradation. Our data reveal Leishmania as an interesting model system to analyze phenotypic adaptation in the absence of transcriptional control and to dissociate the role of complex regulatory networks in the development of stable, disease-causing life cycle stages in eukaryotic pathogens.

By applying a five-layer systems analysis to spleen-derived bona fide amastigotes and culture-derived promastigotes, we identified a series of functionally related gene clusters that are co-regulated during L. donovani stage differentiation. This co-regulation occurs at multiple levels, including transcript stability, proteasomal protein turnover, and protein phosphorylation. Our study underscores the complex molecular architecture of the Leishmania developmental process and provides novel insights into possible regulatory feedback loops that may coordinate stage-specific transitions. These results raise important questions regarding the integration of these regulatory networks during differentiation, and their contributions to environmental sensing and adaptive evolution. In the following, we discuss these putative networks in the context of the current literature and propose experimental strategies for their downstream, mechanistic analyses.

A first series of possible regulatory networks proposed by our results could rely on recursive or self-controlling interactions, where components of a given pathway are regulated by the pathway itself (Figure 6A). In promastigotes, for example, a co-regulated, functional gene cluster showing stage-specific increase in transcript abundance was defined by the GO term ‘post-transcriptional regulation of gene expression’. In the absence of stage-specific gene dosage changes (Figure 1) and the lack of transcriptional regulation of gene expression in Leishmania (Grünebast et al., 2025), the stage-specific expression changes of these transcripts are thus likely regulated itself at the post-transcriptional level. Such auto-regulatory feedback is common for RNA-binding proteins (RBPs), which play a central role in controlling mRNA stability, often targeting their own transcript (Müller-McNicoll et al., 2019). This helps keep protein levels stable (negative feedback) or create on/off switches for developmental processes (positive feedback) (Perrimon et al., 2012). In T. brucei, for example, the stage-specific expression of RBPs, such as ZC3H22 or RBP9, is regulated at post-transcriptional levels (Erben et al., 2021), while other RBPs, such as TbZFP3 or RBP10, can control developmental transitions by stabilizing cohorts of mRNAs, thus defining developmental regulons (Walrad et al., 2012; Mugo and Clayton, 2017). In Leishmania, RBPs are likely to participate in comparable regulatory networks. These can be investigated through methods such as crosslinking and immunoprecipitation to map RBP binding to 3’ UTRs, computational approaches to identify conserved regulatory sequence motifs, and loss-of-function analyses targeting either trans-acting factors (RBPs and their interaction partners) or cis-acting elements (e.g. RBP-binding sites within 3’ UTRs).

Figure 6

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Figure 6—source data 1

Phosphoprotein – stage-specific interaction networks for selected phosphoproteins (STRING).

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Our data propose a similar recursive feedback loop that seems to act on the level of protein translation, which was revealed by a paradox: while amastigotes showed increased abundance for 38 transcripts annotated for the GO term ‘ribosome biogenesis’ (Figure 2, Figure 2—source data 1), the corresponding proteins showed reduced abundance in amastigotes compared with promastigotes (Figure 3C). This dissociation between RNA and protein abundances indicates that ribosomal components themselves may be regulated at translational levels. Increased ribosomal protein abundance in promastigotes could satisfy the requirement of increased translation capacity at this fast-growing stage (Piel et al., 2022; Avila et al., 2018). In contrast, increased mRNA abundance of ribosomal components in amastigotes may indicate stage-specific mechanisms of mRNA storage (Fritz et al., 2015) that may allow to jump-start promastigote differentiation and accelerated growth following exposure to low temperature and neutral pH. This possibility is supported by our proteomics investigations of the early differentiation process, revealing ribosomal biogenesis as one of the first pathways induced over the initial 18 hr during the amastigote-to-promastigote developmental transition (Figure 4—figure supplement 2).

Differentiation to promastigotes was associated not only with quantitative changes of ribosomal components as indicated by co-regulated expression of ribosomal proteins and translation factors (see Figure 4—source data 2), but also seems to affect ribosome structure itself as indicated by stage-specific expression changes of gene sets enriched in the GO terms ‘RNA methylation’ (eight methyltransferases, including the rRNA methyltransferases Ld1S_350672300 and Ld1S_350694000) and ‘pseudouridine synthesis’ (six pseudouridine synthases and the H/ACA snoRNP Nop10, Ld1S_360722900) (Figure 4—figure supplement 1, Figure 4—source data 2). The differential expression of snoRNAs (see Figures 2C and 3, Figure 3—source data 2) and the developmentally regulated changes in rRNA modification that can affect ribosome activity (Rajan et al., 2024; Figure 2D) indeed support the existence of such stage-specific ribosomes. These results are in line with our recent reports providing indication for specialized ribosomes in Leishmania adaptation, in which we correlated changes in rRNA pseudouridylation to L. donovani fitness gain in culture (Piel et al., 2022; Bussotti et al., 2021). Cryo-EM analysis of purified ribosomes from amastigotes and derived promastigotes, combined with targeted deletion of individual snoRNA genes, will be critical to uncover stage-specific structural and functional adaptations of such stage-specific ribosomes.

Finally, a third possible recursive feedback loop seems to act at the post-translational level involving members of various protein kinase (PK) families, which not only showed stage-specific expression, but also represented a main class of phosphorylation substrates themselves. This interaction may reflect stage-specific signaling cascades, where downstream kinases are activated through phosphorylation by upstream kinases, such as exemplified by the MAP kinase pathway (Cayla et al., 2022; Cargnello and Roux, 2011). Indeed, 11 members of the MAP kinase family showed stage-specific changes in their phosphorylation pattern (Figure 5D), 9 of which were only detected in the promastigote phosphoproteome and were previously associated with this stage, being implicated in flagellar biogenesis (MPK9 and MPK3) or metacyclogenesis (MPK4) (Dacher et al., 2014; Baker et al., 2021; Erdmann et al., 2006). In contrast, the two MAP kinases MPK2 and MPK10 – previously linked to amastigote differentiation and virulence (Cayla et al., 2014; Baker et al., 2021) – showed an amastigote-specific increase in protein abundance and relative phosphorylation change for defined phosphorylation sites. In L. mexicana, such a recursive PK feedback loop has been identified between the interacting PKs LmxMKK and LmxMPK3, which cross-phosphorylate each other to regulate flagellar length (Erdmann et al., 2006). To further dissociate the complex regulatory relationships between parasite PKs, protein-protein interaction maps should be established (e.g. co-IP, proximity labeling), combined with in vitro kinase assays to reveal direct kinase-substrate relationships.

Most PKs have pleiotropic effects across biological systems, due to their ability to phosphorylate a wide range of functionally unrelated substrates (Johnson et al., 2023). Our analysis of relative phosphorylation changes normalized to protein abundance confirms such pleiotropic function in Leishmania and suggests parasite PKs at the center of a second form of possible feedback loop defined by reciprocal interactions, where components of two or more regulatory pathways may cross-control each other (Figure 6A). For example, as judged by changes in phosphorylation abundance, it seems that stage-specific PK activities act on a series of phosphatases, which in return may modify kinase activities by dephosphorylation (Figure 5, Figure 5—source data 3). Such reciprocal regulation between both enzyme families is well established across many eukaryotic systems, e.g., between mammalian ERK1/2 and the MAPK phosphatase DUSP6/MKP-3 (Ekerot et al., 2008), or yeast CDK1 and the phosphatase Cdc25, controlling mitotic entry (Hoffmann et al., 1993). In addition, 47 differentially phosphorylated proteins were associated with ‘Translation’, some of which have been linked in Leishmania to translational control, cell cycle regulation, or infectivity (Shrivastava et al., 2021; Zinoviev et al., 2011; Shrivastava et al., 2019; Tupperwar et al., 2019; Tupperwar et al., 2021; Baron et al., 2024). This opens the possibility of yet another reciprocal feedback loop, where phosphorylation of ribosomal components could fine-tune translational control, which in turn may affect the expression of PKs themselves (Baird and Wek, 2012; Sloan et al., 2017; Imami et al., 2018; Merrick and Pavitt, 2018; Piazzi et al., 2019; Jungers et al., 2020; Janin et al., 2020; Bohlen et al., 2021; Khoshnevis et al., 2022).

Finally, our results suggest the possibility of a third reciprocal feedback loop between PKs and proteins of the proteasomal system that may govern Leishmania stage transitions. Previous genetic analyses demonstrated an essential role for various components of this system in promastigote-to-amastigote transition and intracellular survival, including L. mexicana ubiquitin-conjugating (E2) enzymes (UBC1/CDC34, UBC2, and UEV1), the E3 ubiquitin ligase HECT2, or various deubiquitinases (e.g. DUBs 4, 7, 13) (Burge et al., 2020; Damianou et al., 2020). By applying the irreversible proteasome inhibitor lactacystin, we extended this role to the reverse process – amastigote-to-promastigote differentiation – demonstrating that proteasomal activities are critical for both directions of the developmental cycle (Figure 4). The constitutive expression of many proteasomal components across both amastigote and promastigote stages supports a role of phosphorylation in regulating stage-specific proteasomal activities, considering that phosphorylation of DUBs has been linked to changes in their stability, localization, specificity, or catabolic activity (Das et al., 2020; Wang et al., 2021). We indeed identified 11 DUBs and 10 ubiquitin ligases/transferases showing stage-specific phosphorylation at specific residues (Figure 5, Figure 5—source data 3). This correlated with the stage-specific proteasomal degradation of 14 PKs (Figure 4F, Figure 4—source data 3), including two previously implicated in Leishmania in vivo fitness (repressor of differentiation kinase 1, Ld1S_320540600; differentiation inhibitory kinase, Ld1S_280397500, see Figure 4—source data 4; Baker et al., 2021). These findings support the existence of a possible kinase/proteasome reciprocal feedback loop in early parasite development. The experimental validation of such regulatory interactions will require in-depth biochemical, molecular, and systems biology approaches involving global kinase-substrate mapping (Johnson et al., 2023), in vitro reconstitution assays using recombinant proteins to demonstrate reciprocal kinase and proteolytic activities, and genetic perturbation tests to reveal the biological impact of the regulatory interaction.

The complexity of regulatory interactions between PKs and the proteasomal pathway is further increased by showing that substrate phosphorylation can mediate subsequent ubiquitination and degradation, while ubiquitination of MAP kinase family members can regulate kinase activity and localization rather than promote degradation (Hunter, 2007b; Nguyen et al., 2013; Barbour et al., 2023). Future experiments simultaneously analyzing changes in both phosphoproteome and ubiquitome during Leishmania stage differentiation and making precision mutants that lack the ubiquitination or phosphorylation sites may help elucidate this complex regulatory interaction and identify individual proteins and their PTMs that drive parasite development.

In conclusion, our data represent an important novel resource to experimentally assess the regulatory landscape of Leishmania stage development. We propose genetic feedback control as a central mechanism in parasite differentiation, with stage-specific interactions between genes and their products likely adapting the parasite phenotype to the environmental changes encountered inside its vertebrate and invertebrate hosts. In contrast to the major phenotypic shift observed between mammalian-stage amastigotes and insect-stage promastigotes, each developmental stage is stably maintained within its respective host even in the wake of environmental fluctuations. Such phenotypic robustness is known to rely on highly interconnected, stage-specific regulatory networks (Wanddington, 1959), which in the case of Leishmania could implicate recursive and reciprocal genetic interactions between protein translation, protein degradation, and protein phosphorylation (Figure 6B and C, Figure 6—source data 1). Our results define Leishmania differentiation as an excellent model system to study how alterations between phenotypic plasticity (i.e. developmental transitions) and robustness (stable maintenance of stages) shape microbial fitness, and provide a powerful experimental framework for future studies on the emergence of stable, adaptive biological traits through feedback regulation.

The mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifiers PXD035697 (phosphoproteome data) and PXD035698 (proteome upon lactacystin treatment) (Perez-Riverol et al., 2019). The DNAseq data have been deposited in NCBI's Gene Expression Omnibus (Edgar et al., 2002) and are accessible in the BioProject PRJNA1231373 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1231373). The RNAseq data have been deposited in ArrayExpress (EMBL-EBI) (Athar et al., 2019) under the accession number E-MTAB-16528. The mass spectrometry metabolomic data are available on Figshare at https://doi.org/10.6084/m9.figshare.31027849. The scripts for GIP and giptools are available at https://github.com/susefranssen/Global_genome_diversity_Ldonovani_complex (Franssen, 2020). Figure 1—source data 1: DNA - Somy score DNA - Gene copy number variation (gCNV) Figure 1—source data 2: DNA - Gene copy number variation (gCNV) Figure 1—source data 3: DNA - frequency of SNV Figure 2—source data 1: RNA - Transcriptomic analysis ama vs pro Figure 2—source data 2: RNA - snoRNAs Figure 3—source data 1: Protein - Proteomic analysis ama vs pro Figure 3—source data 2: RNAxProtein - Post-transcriptional regulation of gene expression Figure 4—source data 1: Protein - Global proteomic analysis of lactacystin treated ama and pro vs untreated parasites Figure 4—source data 2: Protein - Proteins modulated during the first 18h of ama to pro differentiation Figure 4—source data 3: Protein - Protein stabilization in ama and pro in presence of lactacystine Figure 5—source data 1: Phosphoprotein - Phosphoproteomic analysis of ama vs pro Figure 5—source data 2: Phosphoprotein - Stage-specific relative phosphorylation changes Figure 5—source data 3: Phosphoprotein - Stage-specific functional networks (STRING) Figure 6—source data 1: Phosphoprotein - Stage-specific interaction networks for selected phosphoproteins (STRING) Figure 4—source data 4: PDF file containing original western blot and SDS-PAGE for Figure 4C, indicating the relevant bands and treatments Figure 4—source data 5: Source images of the western blot and SDS-PAGE presented in Figure 4C. Figure 4—figure supplement 3—source data 1: PDF file containing original western blots presented in Figure 4—figure supplement 3C indicating the relevant bands and treatments Figure 4—figure supplement 3—source data 2: Source images of the western blots presented in Figure 4—figure supplement 3C. Figure 4—figure supplement 3—source data 1: PDF file containing original western blots presented in Figure 4—figure supplement 3D indicating the relevant bands and treatments Figure 4—figure supplement 3—source data 2: Source images of the western blots presented in Figure 4—figure supplement 3D.

1. 1. Spaeth G

   (2022) PRIDE

   ID PXD035697. Five-layer systems analysis of Leishmania stage differentiation reveals an essential role for protein degradation in parasite development.

   https://www.ebi.ac.uk/pride/archive/projects/PXD035697
2. 1. Spaeth G

   (2022) PRIDE

   ID PXD035698. Five-layer systems analysis of Leishmania stage differentiation reveals an essential role for protein degradation in parasite development.

   https://www.ebi.ac.uk/pride/archive/projects/PXD035698
3. 1. Spaeth G

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   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1231373
4. 1. Cokelaer T
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Author details

1. Pascale Pescher

   Institut Pasteur, Université Paris Cité, INSERM 1347, Unité de Parasitologie moléculaire et Signalisation, Paris, France

   Contribution

   Conceptualization, Resources, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   For correspondence

   pascale.pescher@pasteur.fr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2796-7855

2. Thibaut Douché

   Institut Pasteur, Université Paris Cité, CNRS UAR 2024, Proteomic Platform, Mass Spectrometry for Biology Unit, Paris, France

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8627-6726

3. Quentin Giai Gianetto

   1. Institut Pasteur, Université Paris Cité, CNRS UAR 2024, Proteomic Platform, Mass Spectrometry for Biology Unit, Paris, France
   2. Institut Pasteur, Université Paris Cité, Biostatistics and Bioinformatics Hub, Paris, France

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

4. Karen Druart

   Institut Pasteur, Université Paris Cité, CNRS UAR 2024, Proteomic Platform, Mass Spectrometry for Biology Unit, Paris, France

   Contribution

   Formal analysis, Visualization

   Competing interests

   No competing interests declared

5. Julie Kovářová

   Glasgow Centre for Parasitology, University of Glasgow, Glasgow, United Kingdom

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8737-6403

6. Blaise Li

   Institut Pasteur, Université Paris Cité, Biostatistics and Bioinformatics Hub, Paris, France

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3080-1899

7. Thomas Cokelaer

   1. Institut Pasteur, Université Paris Cité, INSERM 1347, Unité de Parasitologie moléculaire et Signalisation, Paris, France
   2. Institut Pasteur, Université Paris Cité, Biostatistics and Bioinformatics Hub, Paris, France

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6286-1138

8. K Shanmugha Rajan

   1. Weizmann Institute of Science, Department of Chemical and Structural Biology, Rehovot, Israel
   2. Bar-Ilan University, Mina and Everard Goodman Faculty of Life Sciences and Advanced and Nanotechnology Institutee, Ramat-Gan, Israel

   Contribution

   Investigation, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4826-9802

9. Laura Piel

   Institut Pasteur, Université Paris Cité, INSERM 1347, Unité de Parasitologie moléculaire et Signalisation, Paris, France

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

10. Céline Besse

    Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry, France

    Contribution

    Investigation

    Competing interests

    No competing interests declared

11. Anne Boland

    Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry, France

    Contribution

    Resources

    Competing interests

    No competing interests declared

12. Jean-François Deleuze

    Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine, Evry, France

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5358-4463

13. Mariette Matondo

    Institut Pasteur, Université Paris Cité, CNRS UAR 2024, Proteomic Platform, Mass Spectrometry for Biology Unit, Paris, France

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3958-7710

14. Michael P Barrett

    Glasgow Centre for Parasitology, University of Glasgow, Glasgow, United Kingdom

    Contribution

    Resources, Writing – original draft

    Competing interests

    No competing interests declared

15. Shulamit Michaeli

    Bar-Ilan University, Mina and Everard Goodman Faculty of Life Sciences and Advanced and Nanotechnology Institutee, Ramat-Gan, Israel

    Contribution

    Writing – original draft

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9895-0439

16. Gerald F Späth

    Institut Pasteur, Université Paris Cité, INSERM 1347, Unité de Parasitologie moléculaire et Signalisation, Paris, France

    Contribution

    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

    For correspondence

    gerald.spaeth@pasteur.fr

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0256-2029

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