Poorinmohammad and Salavati BMC Molecular and Cell Biology
https://doi.org/10.1186/s12860-024-00499-4(2024) 25:3

BMC Molecular and Cell
BiologyOpen Access

RESEARCH

Prioritization of Trypanosoma brucei
editosome protein interactions interfacesat residue resolution through proteome-scale
network analysis
Naghmeh Poorinmohammad1 and Reza Salavati1,2*
Abstract
Background Trypanosoma brucei is the causative agent for trypanosomiasis in humans and livestock, which presentsa growing challenge due to drug resistance. While identifying novel drug targets is vital, the process is delayed
due to a lack of functional information on many of the pathogen’s proteins. Accordingly, this paper presents acomputational framework for prioritizing drug targets within the editosome, a vital molecular machinery responsible
for mitochondrial RNA processing in T. brucei. Importantly, this framework may eliminate the need for prior gene orprotein characterization, potentially accelerating drug discovery efforts.
Results By integrating protein-protein interaction (PPI) network analysis, PPI structural modeling, and residue
interaction network (RIN) analysis, we quantitatively ranked and identified top hub editosome proteins, their key
interaction interfaces, and hotspot residues. Our findings were cross-validated and further prioritized by incorporatingthem into gene set analysis and differential expression analysis of existing quantitative proteomics data across various
life stages of T. brucei. In doing so, we highlighted PPIs such as KREL2-KREPA1, RESC2-RESC1, RESC12A-RESC13, and
RESC10-RESC6 as top candidates for further investigation. This includes examining their interfaces and hotspot
residues, which could guide drug candidate selection and functional studies.
Conclusion RNA editing offers promise for target-based drug discovery, particularly with proteins and interfaces thatplay central roles in the pathogen’s life cycle. This study introduces an integrative drug target identification workflow
combining information from the PPI network, PPI 3D structure, and reside-level information of their interface which
can be applicable to diverse pathogens. In the case of T. brucei, via this pipeline, the present study suggested potential
drug targets with residue-resolution from RNA editing machinery. However, experimental validation is needed to fully
realize its potential in advancing urgently needed antiparasitic drug development.
Keywords Trypanosomatids, RNA editing, Drug discovery, Protein-protein interaction*Correspondence:
Reza Salavatireza.salavati@mcgill.ca
1Institute of Parasitology, McGill University, Ste. Anne de Bellevue,
Montreal, Quebec H9X 3V9, Canada
2
Department of Biochemistry, McGill University, Montreal,
Quebec H3G 1Y6, Canada© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Poorinmohammad and Salavati BMC Molecular and Cell Biology

Background
Trypanosoma brucei, a protozoan pathogen, is the causative agent of African trypanosomiasis, endangering millions of human lives and livestock in sub-Saharan Africa[1]. As a prominent member of kinetoplastids, T. brucei
harbors a mitochondrial genome known as kinetoplast
DNA (kDNA). Within this genome, most genes exist as
cryptogenes whose transcripts require post-transcriptional RNA editing to gain protein-coding capability [2].
RNA editing is thus an essential facet of gene expression
in these organisms. Central to this process is the editosome, a multiprotein complex that orchestrates the Uridylate (U) insertion/deletion editing process, ultimately
generating translatable transcripts [3].
According to current knowledge, RNA editing in
kinetoplastids involves a coordinated interplay of over
70 catalytic and non-catalytic proteins. These include
diverse enzymes, RNA-binding proteins, and other factors whose exact functions largely remain unknown[4]. The holo-editosome (editosome holoenzyme) comprises approximately 40 proteins, distributed acrossthree distinct multiprotein complexes: the RNA Editing Catalytic Complex (RECC), RNA Editing Helicase
2 Complex (REH2C) and the RNA Editing Substrate
Binding Complex (RESC) [4, 5]. RECC and REH2C aremainly catalytic; RECC is responsible for the endonucleolytic cleavage, U-indel (insertion/deletion) editing,and ligation of the edited segments. REH2C enhances
the accessibility of the editing sites, allowing the othercomponents of the editosome to perform their functions
effectively. Meanwhile, the heterogeneous RESC provides
the structural framework for the interaction between theguide RNAs (gRNAs) and the target mRNA sequences[4].
The intricate nature of the editosome has made understanding its interactions and underlying mechanism achallenging endeavor [6, 7]. Considering the potential
for identifying drug targets by exploring editosome PPIs
in T. brucei, integrating computational approaches has
become increasingly important. Characterizing the PPIinterfaces within the pathogen is a crucial for identifying drug targets and designing potential drugs to disrupt
these interactions, as seen with the editosome.
Apart from the expensive and time-consuming experimental procedures to identify PPI interfaces, the recent
introduction of AlphaFold-Multimer has been a great
help in achieving accurate structural resolution of protein interactions [8]. However, pinpointing the mostimportant PPI interfaces and finding the hotspots withinthose interfaces require further residue-level analysis.
To address this, the residue interaction network (RIN)
analysis provides a systems-level strategy [9, 10]. Thisapproach identifies central nodes, or hub residues, within
the network through centrality analysis of the RIN.

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Combined with AlphaFold-Multimer findings, this allows
for the identification of interaction hotspots.
Moreover, functional annotation gaps are common in
Trypanosoma proteins, particularly within the editosome, which is distinct across various life domains. An
efficient computational framework could offer a way toprioritize drug targets, bypassing the necessity of geneor protein characterization. This could address a significant challenge in drug discovery. Accordingly, this study
proposes a systems-level approach implementing primarily the experimental PPI data of T. brucei to reconstruct
an editosome PPI network. This serves as the basis for
identifying critical PPIs. Residue-level analysis throughRINs of each PPI structure helps in recognizing interface
hotspots and quantitatively assessing the most important interfaces. Integrating these findings with existing
proteomics data from T. brucei developmental differentiation, enabled the final prioritization of predicted PPIinterfaces within the editosome PPI network.

Methods
Editosome PPI network reconstruction

Throughout the study, a set of seventy-four proteinsassociated with mitochondrial RNA processing in
Trypanosoma were employed, data of which can befound in [4]. Identifiers for these proteins were used toretrieve information on their respective interactions
from TrypsNetDB for T. brucei TREU927. TrypsNetDB
is a comprehensive database that houses experimentally
substantiated interactome data specific to trypanosomatid proteins [11]. To ensure comprehensive coverage, an
extensive literature review was conducted to incorporate
the most recent experimental interactions into the final
dataset. We also considered the experimental techniques
used to validate each interaction, such as affinity purification, immunoprecipitation, and Y2H (yeast two-hybrid).
Furthermore, we assigned an assay score on a scale from0 to 1 to each interaction, reflecting the level of support
provided by the number of experimental techniques confirming its validity.
Final interaction data were visualized using Cytoscape
3.9.1. CytoNCA plugin was implemented for the topological analysis of the network [12]. Calculated centrality measures include degree, betweenness and closeness.
These three centralities are known to best represent the
hub nodes in biological networks [13–15]. To consider
the level of confidence for an interaction, assay scores
were assigned as weights to the network edges during thecentrality analysis.
Many existing studies focus solely on one specific centrality measure, such as degree, which can lead to theloss of valuable information. This study employs a multicriteria approach, using three centrality measures to
identify hub proteins within the editosome PPI network.

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Multi-criteria decision analysis (MCDA) [16] was used torank the identified proteins, with equal weightage given
to the three centralities. The resulting MCDA score,
ranging from 0 to 1 for each protein, signifies its performance across multiple centralities. A higher MCDA
score indicates a strong potential for a protein to be ahub, effectively consolidating multiple centrality information into a unified ranking measure. Here, editosome
proteins with MCDA score of more than 0.7 were considered as hubs.

We performed a centrality analysis on the reconstructed, weighted RINs, following the conditions outlined in the previous section. This analysis identified
central residues within the protein; if they coincided withthe confirmed interface identified earlier, they were categorized as hotspot residues. We ranked the identified
hotspot residues using a dual-criteria approach that considered their RIN-based MCDA score (cutoff = 0.5) andtheir PAE values.

Structure prediction of hub PPIs

We used quantitative proteomics data from different
developmental stages (timepoints) of T. brucei TREU927,
as generated by Dejung et al. [17]. Missing values in protein abundances were imputed using missForest R package [18, 19] based on the log2-transformed Label-Free
Quantification (LFQ) intensities of each timepoint. We
performed differential expression analysis (DEA) using
DEqMS, a tool that extends the capabilities of the Limma
R package [20]. This adaptation involved modifying Limma’s variance prior estimation to account for the relationship between variance and the number of detected
peptides for each protein. This modification results ina more accurate, data-dependent estimation of protein
variance [20]. We used the resulting fold changes andadjusted p-values from the DEA as input for gene/protein
set analysis (GSA), facilitated by the “piano” package [20].
Protein sets were assembled based on a gene ontology(GO) term dataset derived from the detected proteomeof T. brucei, using various sources including AmiGO [21],Blast2GO [22], TriTrypDB [23], Pannzer [24], and UniProt [25]. GSA and DEA were also applied to the editosome subset of the proteomics data where needed.

All interactors connected to each selected hub protein,supported by at least one RNase positive experimental
technique, were fetched from the editosome PPI network. This filtration step aimed to eliminate interactions that rely solely on the presence of RNA, thereby
improving the accuracy of PPI modeling and subsequent
analyses.
Each binary PPI involving the hub proteins was modeled by AlphaFold_2.0_multimer.v3 as implemented in
the Cosmic2 gateway [16]. AMBER molecular dynamics
simulation was also configured to optimize the aminoacid side chain position of the top-ranked model.
PPI structures were visualized and further analyzed
using UCSF ChimeraX version: 1.5 [16]. Residues fromthe interacting proteins located within a proximity of ≤ 5
Angstroms (Å) were selected and designated as primaryinterface residues. Predicted alignment errors (PAE) is aquantitative assessment metric from AlphaFold that provides a distance error for every pair of residues. It represents the tool’s position error estimate at residue x when
the predicted and true structures are aligned on residuey, with value ranging from 0 to 35 Å. In PPI modeling,
low PAE values for residue pairs (x, y) from each interacting protein suggest that AlphaFold has accurately predicted their relative positions and orientations [16]. Thus,
primary interface residues with PAEs of ≤ 10 Å wereselected and considered as confirmed interface residues
used for further analysis.
RIN analysis

While PAE provides residue-level evaluation of modeledinterfaces, it does not inherently capture the biologicalcharacteristics of the hub protein. To achieve a holistic
understanding of each hub protein’s interactions at theresidue level, we reconstructed a residue interaction network (RIN). RINs serve as graphical representation ofprotein structures, where residues act as nodes and their
physicochemical interactions form the network edges.
Using RING 3.0 [10], we generated RINs based on thebound-state structure of the hub protein and its interaction partners. We also incorporated the energy of interaction between residues as weights for the RIN edges.

Proteomics data analysis

Results and discussion
Editosome PPI network of T. brucei
Reconstruction of the editosome PPI network

The reconstructed PPI network of editoproteome in T.
brucei resulted in 90 nodes with 677 edges (Additional
file 1). While nodes in the network represent proteins,the edges signify experimentally verified interactions.
These interactions were identified using various techniques, including but not limited to Y2H, immunoprecipitation, affinity purification, and fractionation [11]. It is
important to note that a single protein interaction may besupported by multiple techniques and could, therefore,
have multiple edges in the network. This concept wasused as the basis for a scoring system to assign weights tothe edges. Consequently, interactions verified by multiple
studies received higher scores for subsequent network
analyses.

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Table 1 List of top ten hub proteins of editosome PPI network in T. brucei and their centrality measures
Rank
1
2
3
4
5
6
7
8
9
10

TriTryp ID
Tb927.1.3030
Tb927.11.16860
Tb927.6.1680
Tb927.7.800
Tb927.9.4360
Tb927.5.3010
Tb927.10.10830
Tb927.2.3800
Tb927.8.8170
Tb927.4.1500

Name
KREL2
RESC3
KH2F1
RESC10KREL1
RESC6RESC13RESC2RESC12aKREH2

Betweenness*1532.5881
422.2286
1334.1823
464.2319
733.3652
243.2760
275.5744
347.8233
302.1332
140.5734

Closeness*
0.15800.19150.17120.19030.17330.18910.18810.19470.18090.1899Degree*11
16.5
8
14.12511.37515.25
14.87513.37514.125
13

MCDA score0.82610.75300.74490.71220.68600.68470.68250.67920.66080.6183* Degree: Number of direct interactions a protein has; Closeness: Measures how quickly a protein can interact with all others; Betweenness: Identifies proteins criticalfor connecting others in the network

Hubs in the editosome PPI network

Hubs were identified and ranked based on Multi-Criteria
Decision Analysis (MCDA) of betweenness, closenessand degree centralities (Additional file 2). The top 20
hubs predominantly include RESC proteins, comprising
85% of all known RESC proteins. The predominance of
RESC proteins as hubs underscores their pivotal role in
Trypanosoma’s RNA editing machinery. One reason isthat these proteins often act as the scaffold for editing;thus identification of RESC complex proteins as hubsaligns with their fundamental roles in the RNA editing [3,26, 27].
For further analysis, 10 proteins were selected as toprank hub proteins with MCDA ≥ 0.6. As illustrated inTable 1, the majority of these top-ranked hub nodes arealso RESC proteins. From all known RESC proteins in theeditosome (n = ~ 20), six were identified as top hubs in theeditosome PPI network. The remaining top-ranked hubs
encompass two editosome ligases, KREL1 and KREL2,
along with the REH2C complex helicase and zinc-finger
proteins, KREH2 and KH2F1. The two ligases are paralogs and KREL1 is reported to be essential ligase forparasite survival [28]. KH2F1 serves as an adaptor linking the KREH2 helicase with the RESC [4]. A finding also
supported by the study from Kumar et al., which demonstrated the critical roles of KREH2 and H2F1 in forming
stable mRNA-gRNA hybrid substrates within molecularscaffolds for the editing process [29]. Thus, the centralityof these proteins aligns well with their essential functions
in RNA editing.
Structure-based analysis of central editosome PPIs
Modeling hub PPIs using AlphaFold-Multimer

As evident in Table 1, each top-rank hub interacts withmultiple partners, denoted by a minimum weighteddegree of 8. We restricted our modeling to PPIs that
met with the following criteria: (a) supported by at least
one experimental assay performed in the presence of
RNase, and (b) highly ranked based on their edge scores,

Table 2 Central editosome PPIs in T. brucei
PPI No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

TargetName*RESC3KREPA1
RESC3
Tb927.11.16860 RESC2
RESC5
RESC6
KH2F1
Tb927.6.1680
RESC3
RESC2
RESC10 Tb927.7.800
RESC3
RESC13
RESC4
RESC6
KREL1
Tb927.9.4360
KREPA2KREH1RESC6Tb927.5.3010
RESC13
RESC12KH2F1RESC5RESC13 Tb927.10.10830 RESC9
RESC2
Tb927.2.3800
RESC1
RESC12A Tb927.8.8170
RESC13
KREH2
Tb927.4.1500
RESC6
KH2F1
Hub
Name
KREL2

TriTryp ID
Tb927.1.3030

TriTryp ID
Tb927.11.16860
Tb927.2.2470
Tb927.2.3800
Tb927.10.11870
Tb927.5.3010
Tb927.11.16860
Tb927.2.3800
Tb927.11.16860
Tb927.10.10830
Tb11.02.5390b
Tb927.5.3010
Tb927.10.8210
Tb927.11.8870
Tb927.10.10830
Tb927.4.4160
Tb927.6.1680
Tb927.10.11870
Tb927.2.1860
Tb927.7.2570
Tb927.10.10830
Tb927.5.3010
Tb927.6.1680* The names of target proteins identified as hubs themselves are formatted initalics

signifying that interactions supported by multiple techniques are considered more reliable.
Twenty-two hub PPIs were ultimately identified, andtheir 3D interaction structures were modeled using
AlphaFold-Multimer (Table 2). Well-known interaction
such as KREL2-KREPA1 [3, 30], KREH2-KH2F1 [29,31], and RESC2-RESC1 [32] ranked among the top PPIs.
Moreover, given that the editosome PPI network is highlyinterconnected, and according to Table 2, the fact thatmultiple target proteins serve as hubs themselves makethese interactions particularly interesting for further
analysis.

Poorinmohammad and Salavati BMC Molecular and Cell Biology

The structures of 20 central PPIs were successfullymodeled. However, jobs involving PPIs that included the2167-aa KREH2 ran out of memory due to the current
limitations on the total number of residues [8].
Subsequently, PPI models were filtered based on highaverage PAE (> 10 Å) at their interface, defined as < 5 Å
between residues from the two interacting proteins.
Models with high PAE values, which imply ambiguity inthe interaction details, were discarded [33]. This led toa final set of six hub PPIs with very low average PAEs attheir interfaces.
Most editosome proteins and interactions are evolutionary unique and lack sufficient sequence homology
for straightforward prediction using AlphaFold2, which
relies on multiple sequence alignment (MSA). However,as efforts to improve AlphaFold2’s performance continue,
the possibility of accurately studying all hub PPIs in the
future remains viable.
Finally, the six accurately modeled interactions
included KREL2-KREPA1, RESC2-RESC1, RESC10RESC6, KREL1-KREPA2, RESC12A-RESC13 and
RESC3-RESC2.
Interface hotspots identified through RIN analysis

The analysis of complex biological systems in the form of
networks can efficiently aid in characterizing the wholesystem and its individual components [34]. Protein
structures serve as an excellent example of such complex systems and can be represented as graphs, whereamino acid residues act as the nodes and their interactions as edges. Calculated topological parameters from
RINs have shown to correlate with various aspects ofprotein structure and function. This approach has beensuccessfully used to identify key residues involved inprotein stability [35], folding [36], functional and regulatory roles (e.g., active site residues) [37] as well as finding
cancer mutation hotspots [38]. More importantly, RINs
provide a global perspective on PPI interfaces, mainly
by finding hotspots useful for drug discovery and design[9]. Furthermore, since certain critical residues locatedwithin the PPI interfaces exert larger energetic impacts
on the affinity and stability of the interactions, uncovering these PPI hotspots holds immense potential for(2024) 25:3

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not only modulating the PPIs in drug discovery studies
but also guiding the design of site-directed mutagenesis
experiments aimed at characterizing protein function
and interactions. Therefore, to study hub PPIs, we reconstructed RIN of each hub protein in its bound state with
its top interactor. Subsequently, centrality analysis wasundertaken, focusing primarily on two objectives: (1) Prioritizing the interfaces detected from previous steps; (2)Finding hotspot residues in each interface.
Through RIN topological network analysis, centrality measures were assigned to each residue in the formof a MCDA score. This score was based on betweenness,closeness and degree centrality for each hub. By merging
these residue-level measures with PAE values, it became
possible to both quantitatively rank, and filter identified
interface residues and to detect hotspot residues. Comprehensive results are available in Additional file 3, and asummary of this approach’s general outcome is provided
in Table 3.
Using quantitative measures and cutoffs, multi-stepprioritization was performed on the editosome PPIs
to find the most critical PPIs, interfaces, and hotspots,
which could significantly help in future drug discovery
efforts. These results can also inform functional understanding through experimental mutation assays.
According to our findings, hotspots are mostly
enriched with tyrosine and arginine residues. Tyrosine,an aromatic amino acid, often engages in pi-pi stacking
interactions with other aromatic residues, significantly
contributing to the stability of protein-protein complexes. Arginine, on the other hand, may form multiplehydrogen bonds both within the protein and with theinteracting partner. These amino acids have been frequently assigned as interface hotspots in different studies[39, 40].
PPI interfaces of central editosome ligases The editosome ligases, KREL1 and KREL2 share significant
sequence homology. Similarly, their binding partners,
KREPA2 and KREPA1, also display sequence conservation in specific regions, suggesting functional analogies
between the two PPI. Despite these similarities, our findings reveal distinct differences between the two interac-

Table 3 Prioritized hub editosome PPIs properties
Hub PPIKREL1-KREPA2
RESC3-RESC2
RESC10-RESC6
KREL2-KREPA1
RESC2-RESC1
RESC12A-RESC13Interface length*
7
8
13
8
15
8

Average PAE
4.12
7.40
2.91
1.26
2.06
2.56

Average RIN MCDA
0.70
0.53
0.58
0.69
0.60
0.54

Rank †
3
6
4
1
2
4

*

Each residue from the hub protein within the interface may interact with more than one residue from the target protein†

Using dual-criteria ranking approach based on the average PAE and average RIN MCDA

Top 3 interface hotspots
N429, E433, L425
Y446, H407, L475
K303, V100, P99
F393, Y390, R396
H345, Y250, R220
E644, Y598, R641

Poorinmohammad and Salavati BMC Molecular and Cell Biology

tions, in which the KREL2-KREPA1 interaction ranking
higher and featuring different types of hotspot residues in
the identified interface.
Table 1 highlights that KREL2’s central position on
the editosome interactome, having a notably higher
betweenness centrality compared to its paralog, KREL1.
This coupled with its enhanced interface importanceranking, suggests that KREL2 plays a more pivotal role
in RNA editing than previously thought. Surprisingly,
this contradicts prior studies using knockdown assays,
which suggested that KREL1 is essential for RNA editing,while KREL2 appears to have little impact [28]. A widelyaccepted theory posits that KREL1 can compensate for
KREL2 loss, but not vice versa [41]. Yet, the lack of studies on KREL2 null mutants leaves room for alternative
explanation. Our results propose that even a minimallevel of KREL2 may suffice for RNA editing, which could
explain the lack of severe impact upon its knockdown.
Furthermore, morphological changes observed in KREL2
knockdown experiments [41] indicate its importance inthe parasite’s biology, potentially during specific developmental stages.
While the KREL2-KREPA1 PPI ranks highest in termsof the structural and network-based metrics used in thisstudy, its potential as a drug target may be compromisedby reports suggesting that KREL1 could compensate forthe loss of KREL2. However, this compensation mechanism is not yet fully experimentally verified and requires
further investigation. If proven otherwise, the KREL2KREPA1 interaction could serve as a valuable drug target
for combatting T. brucei.
Structural analysis of the interfaces of both ligases’
interactions shows a similar pattern in which the ligases
primarily interact through their C-Terminal regions with

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a single alpha helix domain in their interacting partners,
KREPA1 and KREPA2. The C-termini of KREL1 andKREL2 consists of four α-helices, with the highest-scoring interface residues located within two corresponding
specific helices: aa 392–409 and aa 421–454 in KREL1
as well as aa 353–371 and 381–410, in KREL2 (Fig. 1).
These findings are consistent with Moses et al.’s recent
research on KREL1-KREPA2, which showed that deletions and mutations in the mentioned helices adversely
impact ligation processes [30]. As depicted in Fig. 1, theinterface spans both KREL1 and KREL2’s C-termini,
where the alpha helices interact most favorably, whereas
the remaining interface residues are located within loop
regions. Importantly, the interface residues between thehelices exhibit higher average centrality and PAE scores,
underlining their critical role in the interaction.
PPI interfaces of editosome RESC members RESC2RESC1 interaction
The interface between RESC2 and RESC1 is the mosthighly ranked among RESC interactions, based on its
average network-based MCDA score. RNAi-mediatedknockdown of both proteins confirm their essentiality forparasite growth and their critical role RNA editing [42,43]. Moreover, the RESC2-RESC1 interaction emerges
as a central hub in the RESC PPI network and has beenvalidated recently through cryo-EM structural analysis in
T. brucei [32]. The AlphaFold model created in this studyaligns closely with the cryo-EM structure, showing an
RMSD of 0.9.
Despite only about 35% sequence similarity, the AlphaFold Multimer model of RESC2-RESC1 reveals a remarkable structural similarity between the two proteins(Fig. 2a). The high-scoring interface primarily involves
interactions between a β-hairpin of one protein and a the
β-barrel and C-terminal patch of the other. The interface
involving RESC2’s hairpin exhibits higher average PAE
and RIN centrality values, making it a prime candidatefor drug discovery studies.

RESC10-RESC6 and RESC12A-RESC13 interactions

Fig. 1 The modeled structure of central editosome ligases interface. (a)KREL2-KREPA1 and (b) KREL1-A2. Blue lines are the interactions between
interface residues of the two interactors (PAE max 5Å, distance max 5Å).
Top 3 hotspots of each PPI are labeled and are shown in red with the sidechains visible

The next highest scoring PPIs within the RESC complex
are RESC10-RESC6 and RESC12A-RESC13. RESC10
has already been experimentally validated as an essential, yet low abundance, RNA binding protein [44, 45].
RESC10-RESC6’s significance in RNA editing is not yet
fully understood. Although in a very recent study Liu etal. which used cryo-electron microscopy and molecular approaches, shed light on this. Even though they
did not discuss this interaction, it captured a state of
the RESC referred to as the RESC-B particle (PDB ID:
8FNI) [26]. This state showed an interaction between
RESC10 and RESC6. A structural alignment of with thePoorinmohammad and Salavati BMC Molecular and Cell Biology(2024) 25:3

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Fig. 2 The modeled structures of central RESC PPIs from the editosome of T. brucei: (a) RESC2-RESC1 PPI, solid black arrows show the beta-barrels of eachprotein, and the dotted squares show the beta-hairpins; (b) RESC12A-RESC13 PPI; (c) RESC6-RESC 10 PPI. The upper figure for each PPI is the space fillingmode of the PPI structure in which the dotted arrows pinpoint core holes potentially needed for RNA binding. Blue lines are the interactions between
interface residues of the two interactors (PAE max 5Å, distance max 5Å). Top 3 hotspots of each PPI are labeled and are shown in red with the sidechainsvisibleAlphaFold-Multimer model showed RMSD of 0.9, suggesting a high degree of structural similarity.
Both RESC10 and RESC6 possess ARM/HEAT repeats
in their structural framework. These repeats spiral toform a channel at the core of each protein—a site potentially favorable for RNA binding. Due to inherent flexibility of ARM/HEAT repeats, these proteins can adapt theirconformation to align with RNA structures or engage
with other proteins. In the PPI model depicted in Fig. 2b,
RESC10 engages both ends of RESC6’s spiral curve, effectively obstructing its channel. This complementary interaction implies that in the presence of RESC10 stabilizes
RESC6, potentially securing RNA molecule binding. Theinterface residues at both ends, as well as the predicted
hotspots, are supported by good PAE and RIN centrality scores, indicating their importance in this interaction.
These critical interface elements are visually represented
as blue lines connecting the interacting components in
Fig. 2b.
For the RESC12A-RESC13 PPI, existing reports suggest a role in initiating the RNA editing process. These
proteins are thought to assist in recruiting other RESC

proteins and defining the active editing region. However,detailed studies focusing on this interaction are lacking.
The study conducted by Liu et al., separated or excluded
RESC12 and RESC12A from the cryo-electron microscopy (cryo-EM) material, thus not providing direct evidence of their interaction with RESC13 [26].
In RESC12A-RESC13 PPI, the N-terminal RNA recognition motif (RRM) of RESC13 mediates its binding
to RESC12A (Fig. 2c). While most of the interface residues from RESC12A interact with connecting loops of
the RRM domain, the predicted interaction hotspots areprimarily located in the defined secondary structures of
the RRM, mainly beta-sheets. Interactions with residues
in these beta sheets may be especially important for several reasons [46]: (1) they often form strong hydrogenbonds and hydrophobic interactions, contributing to
the stability of the PPI, (2) beta sheets tend to be more
evolutionarily conserved than loop regions, suggesting
the interactions involving these residues are likely essential for the protein’s function. Therefore, even among thepredicted hotspots, some might be biologically more relevant which helps in target-based drug design process.

Poorinmohammad and Salavati BMC Molecular and Cell Biology

In general, considering the high centrality of RESC12A
in the editosome PPI network and its prominent interface ranking with RESC13, further investigation of thisinteraction could be of significant interest [47, 48]. Additionally, a separate study measuring the stoichiometry(relative abundance) of RESC10 in comparison to other
RESC proteins in T. brucei has indicated that RESC10
is relatively less abundant, accounting for only approximately 1–2% of the levels of RESC13 and RESC12A [45].
While the low relative abundance of RESC10 comparedto other RESC proteins may be indicative of the protein
to be present in only a subset of RESC complexes or thatit interacts transiently and with RESC complexes [45], italso suggests that RESC10’s lower abundance and transient interactions are due to tight regulation mechanisms
make it an interesting target for further studies.
Accordingly, due to the significance of proteomics data
in further interpreting the central PPI findings of the editosome, the following section delves into the utilizationof this data to enhance the analysis of the results from theprevious sections.
Enhancing editosome PPI investigations with quantitative
proteomics analysis

The inclusion of quantitative proteomics data in the current research serves a multiple purpose. Firstly, it canserve as a means of cross-validation for the study’s findings as well as serving as an independent method to findnovel central editosome proteins via performing DEA
analysis. Moreover, given that one of the primary objectives of the study is to prioritize drug targets, the integration of this data reinforces the fact that the chosen targets
are firmly rooted in the disease’s underlying biology. In
general, as integrating proteomics data significantly
enhances the likelihood of identifying viable and efficacious drug target candidates, it aids in identifying targets
that are more likely to translate into effective therapies
and ultimately improves the success rate of relevant studies. While acknowledging the benefits of such pipeline,
to further enhance the robustness of our findings, weemphasize the necessity of experimental validation. Furthermore, The feasibility of applying a similar pipeline toother pathogens hinges on the accessibility of comparable
omics data.
Quantitative proteomics data from [17], is generated
from samples of different life stages of T. brucei in whichlong slender bloodstream forms (LS) were differentiatedto short stumpy forms (SS) followed by differentiation
to insect form trypanosomes (PF) after multiple sampled timepoints in between. Understanding the LS to SS
transition is crucial for understanding the lifecycle of T.
brucei, which has implications for disease progression
in the host organism. Furthermore, the proteins that are
differentially expressed or interact differently during this(2024) 25:3

Page 8 of 12transition could be potential targets for therapeutic interventions. Given these factors, the choice to focus on the
LS to SS transition offers a well-defined and biologicallymeaningful context within which to explore the hub proteins and PPIs identified in this study. Therefore, to further investigate the hub proteins and PPIs found in thepresent study, the LS to SS differentiation was chosen to
study DEA as well as GSA.
To gain a pervasive view on the differential proteome
composition and to put single protein alterations into alarger biological context, gene set analysis (GSA) was
performed on all protein abundances from LS and SS
samples. To accomplish this, each protein was annotatedwith Gene Ontology (GO) terms obtained from various
databases. Using the created T. brucei GO dataset consisting of 35,448 GO terms, GSA showed 35 GO terms
to be significantly (adj-p-value < 0.05) downregulated in
SS form., Most upregulation pattern was observed formembers of RNA binding GO terms(Fig. 3a). Given thatthe transition to the stumpy form marks a change fromproliferative to a non-proliferative state, it is reasonable
to expect downregulation of different metabolic genesthat were more active in the LS form. Importnatly, GSA
also identified significant terms related to mitochondrial
RNA editing. As illustrated in Fig. 3a, GO terms such aspoly (A) binding, RNA helicase activity, mRNA binding
and RNA binding include editosome proteins, thereby
validating these proteins as promising drug targets due totheir critical roles in the parasite’s infective stage.
In addition, DEA performed on the editosome fraction of the proteomics data revealed that all detected
hub proteins (see Table 1), except KREL1, showed differential expression during the LS to SS differentiation(Fig. 3b). This suggests that the expression of these central proteins’ expression is significantly upregulated in
the proliferative LS, further supporting their potential aspromising drug targets.
According to the DEA results, the most significant
upregulated editosome proteins ((|log2 FC| > 1), are
KREPB4 and KREX2. KREPB4 has been confirmed as an
essential protein in both the bloodstream and procyclic
forms of T. brucei, whereas KREX2 is not essential in any
form of the parasite [49, 50]. These editosome proteins
had MCDA scores of approximately 0.55, which did notmeet the set threshold for classification as hub proteinsin the editosome PPI network. Furthermore, its interactions failed to meet the criteria of being supported withat least one RNase positive assay thus not included ininterface analysis in this study.
Figure 3c visualizes the changes in abundance of the
top hub editosome proteins in LS and SS forms. All hub
RESC proteins, except for RESC10, display significantly
reduced abundance in SS form. Previous report has
shown that RESC10 interacts with RESC components

Poorinmohammad and Salavati BMC Molecular and Cell Biology(2024) 25:3

Page 9 of 12

Fig. 3 Proteomics data analysis in SS compared to LS form. (a) GSA of protein expression Protein sets are defined by GO terms at biological process level.
For each GO term showing significant enrichment (in this figure: adjusted p-value < 0.05), the direction of the relative changes in protein levels are shown.
(b) volcano plot highlighting differentially expressed proteins in (|log2 FC| > 0.5, p-value < 0.05). The detected hubs in this study are labeled on the volcano
plot. (c) box plots of hub proteins visualizing the protein abundance change

early in their assembly and then is released. Moreover, the effect of RESC10 on RESC assembly has beenreported to be particularly important during transitions
between insertion and deletion RECCs [45]. Therefore,
its upregulation in the SS form likely reflects the parasite’s adaptive strategy in preparation for its transition
to the tsetse fly vector. This facilitates rapid switching
between RECC insertion and deletion subcomplexes andthus establishes RESC10’s status as a pivotal member ofthe editosome complex.
In the next step, after investigating the top ranked hubs
via proteomics data, this data was further leveraged tovalidate the investigated central PPIs (see Table 3) using
co-expression analysis. This analysis involves assessing
the correlation in expression patterns between the pairsof proteins (PPIs) across different developmental stagesof T. brucei. Such analysis can help prioritizing hub PPIs
based on experimental data; a positive correlation acrossdifferent timepoints can verify that the PPI is both stable
and crucial rather than transient.
As illustrated in Fig. 4, most hub PPIs exhibited apositive correlation between their respective binding
interactors, suggesting a potential coordination in theexpression or regulation of these protein pairs undervarious conditions. However, the PPI between KREL2

and KREPA1 exhibited an overall negative correlation yet
showed a significant positive correlation during the later
developmental time points, specifically, from 6 to 48 h.
This temporal correlation pattern is particularly intriguing as it coincides with the parasite’s transition towards
fine-tuning its differentiation into the procyclic form.
In contrast, the paralogous PPI, KREL1-KREPA2, demonstrated a very high correlation during the early phase,
specifically at the SS-6 h timepoint. This early correlationsuggests that KREL1 and KREPA2 may play crucial rolesin the initial stages of parasite development. Given that
KREL1-KREPA2 and KREL2-KREPA2 are known to bedeletion and insertion RECC subcomplexes, respectively[41], our finding highlights the predominance of deletion
processes in the SS form, while insertion processes arelikely to be more dominant during the parasite’s differentiation into the procyclic form which is the proliferative
state of the parasite in the fly vector. As insertion editing is generally more prevalent, that could be indicativeof a broader biological strategy for adaptability and complexity, particularly useful during the life stage where theparasite is actively growing and dividing. Furthermore,
RESC12A-RESC13 have shown to be significantly correlated throughout the developmental stages of T. brucei,

Poorinmohammad and Salavati BMC Molecular and Cell Biology(2024) 25:3

Page 10 of 12

Fig. 4 Correlation assessment of the top editosome PPIs using proteomics data from different time points of developmental stage in T. brucei startingfrom SS as starting point. Correlation coefficient is labeled above the temporal abundance of each graphmaking them an interesting case for further analysis specially for drug target discovery procedures.
Overall, these findings highlight the importance ofcontext-dependent analysis when studying protein interactions within the complex biological systems of parasitic organisms. Further investigation into the functional
implications of these correlation patterns on the hub
editosome PPIs may provide valuable insights into theregulatory mechanisms governing the parasite’s developmental transitions.

Conclusion
The emergence of resistance to treatments for human
African trypanocides emphasizes the pressing need for
new therapeutics. Often, resistance emerges due to mutations or alterations in parasite’s receptors or key proteins,
making the need for novel drug targets against T. brucei
more urgent. However, the lack of functional knowledge
about many genes and proteins in the parasite has significantly hindered this endeavor.
Unique to kinetoplastids, the RNA editing processorchestrated by the editosome protein supercomplex
provides an excellent opportunity for target-oriented
drug discovery. This mechanism is indispensable for theparasite’s survival and adaptability, making it a prime
candidate for pharmacological intervention. PPIs lie atthe core of biological processes, including RNA editing.
Comprehensive structural analysis of these PPIs not only

sheds light on our understanding of the underlying biology but also guides the development of drugs capableof modulating these interactions, potentially leading tonovel treatments.
Hence, this study employed a multi-layer approach,including structural, network, and omics-based methods, to focus on the most promising drug targets within
the central editosome complex in T. brucei. By conducting comprehensive analyses and the implementation ofstate-of-the-art tools like AlphaFold-Multimer, we identified key editosome hub proteins and detailed their pivotal interactions. Our network-centric analysis allows forthe prioritization of specific PPIs, with interactions like
RESC2-RESC1, RESC12A-RESC13, RESC10-RESC6 and
KREL2-KREPA1, standing out for further exploration.
Calculating hotspot residues for each interface provides
valuable insights for drug candidate selection and functional studies, offering new ways for targeting that might
mitigate the likelihood of resistance development.
In conclusion, this study highlights the potential of
exploring editosome protein interactions to uncover vital
pathways for therapeutic intervention against T. brucei.
The findings not only underscore the importance of theseinteractions but also offer a streamlined workflow of integrating multiple analytical approaches for drug target
identification, applicable to a wide range of pathogens,pending further experimental validation.

Poorinmohammad and Salavati BMC Molecular and Cell Biology

Supplementary Information

The online version contains supplementary material available at https://doi.
org/10.1186/s12860-024-00499-4.
Additional File 1: Editosome PPI network table of T. brucei.
Additional File 2: Centrality analysis results of PPI network.
Additional File 3: RIN-based centrality analysis of hub PPIs integrated with
PAE values for interface residue ranking.
Acknowledgements
Not applicable.
Author contributions
NP conceptualized and designed the research, conducted experiments, andwrote the paper. RS supervised the research and provided critical review andrevisions for the paper. All authors read and approved the final manuscript.
Funding
This project has been supported by a research grant 252733 form CIHR to RS
and Fonds de recherche du Québec– Santé (FRQS) foundation for providing
financial support to NP.
Data availability
All data generated or analysed during this study are included in this publishedarticle [and its supplementary information files].

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Received: 26 September 2023 / Accepted: 19 January 2024

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