https://orcid.org/0000-0002-5339-8978
Figures
Abstract
Persistence - characterized by the transient ability of subpopulations of Leishmania parasites to survive exposure to drugs - is a major driver of treatment failure and clinical relapses in leishmaniasis. Persisters are characterized by non-dividing or slow-growing state and increased drug tolerance. However, the molecular mechanisms governing formation of persisters remain poorly understood in Leishmania parasites. Here, we developed a model to explore persistence in Leishmania mexicana. Viable promastigote persister-like subpopulations were enriched using Ficoll density gradient centrifugation following lethal exposure to potassium antimonyl tartrate (PAT) that killed 80% of parasites. The surviving parasites exhibited delayed growth in drug-free medium that is characteristic for persisters, and higher drug tolerance upon rechallenge. Transcriptomic profiling across acute stress, drug-free recovery, and rechallenge phases revealed a global remodeling in persister-like cells under all tested conditions. Induction was characterized by downregulation of several biological processes and a robust upregulation of nucleolar pathways, supporting epitranscriptomic changes during the formation of persister-like cells. Upon drug removal, this profile rapidly reverted, initiating ribosome biogenesis to exit latency and resume proliferation. Resuscitation phase exhibited active protein synthesis and upregulation in biological processes associated with metabolic and mitochondrial functions. Furthermore, persister-like parasites displayed distinct drug response profiles compared to parental parasites by rapidly implementing a highly conserved, coordinated survival reprogramming, where 316 genes were uniquely downregulated, and 241 genes were upregulated. The distinct features of the drug response in rechallenged persister-like cells were characterized by the downregulation of mitochondrial function and protein synthesis to induce dormant state, and the upregulation of drug-response and stress-tolerance genes to survive immediate toxicity. In contrast, parental parasites displayed a broad and disorganized drug response. Additionally, rechallenged persisters exhibited distinct transcriptomic features that transiently phenocopies stable genetic resistance. This pre-adapted state is characterized by the targeted upregulation of epigenetic and epitranscriptomic modulators, heavy metal transporters, and catabolic enzymes to maintain viability. These findings demonstrate that drug persistence in Leishmania is not merely a metabolic collapse, but rather a sophisticated survival strategy involving active transcriptome remodeling, downregulation of translation and epigenetic/epitranscriptomic changes. This transient state constitutes an initial evolutionary step toward permanent drug resistance and highlights new molecular vulnerabilities for therapeutic interventions aimed at preventing clinical relapse.
Author summary
The efficacy of leishmaniasis chemotherapy often fails because Leishmania parasites survive the drugs by entering a dormant state, causing relapses in patients. In this study, however, we discovered that these parasites are not simply ‘quiescent.’ On the contrary, they orchestrate a highly active survival response: they shut down energy-consuming functions while keeping their cellular alert mechanisms active. Remarkably, upon re-exposure to antileishmanial drugs, the parasites recall previous stress events and transiently activate defensive pathways characteristic of stable drug resistance. Understanding how Leishmania employs this temporary adaptive strategy provides critical targets for developing therapies designed to eradicate the infection and avert relapses.
Citation: Quiceno E, Rodríguez-Almonacid CC, Omeir K, Ancira J, Phillips C, Karamysheva ZN (2026) Active transcriptome remodeling underlies antimony-induced drug tolerance in persister-like Leishmania parasites. PLoS Pathog 22(10): e1014682. https://doi.org/10.1371/journal.ppat.1014682
Editor: Hira L. Nakhasi, US Food and Drug Administration, UNITED STATES OF AMERICA
Received: June 2, 2026; Accepted: September 22, 2026; Published: October 5, 2026
Copyright: © 2026 Quiceno et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The raw and processed data supporting the conclusions of this article are available in the Gene Expression Omnibus database from the NCBI (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE332713) under accession number GSE332713.
Funding: This study was funded by Institute for One Health Innovation seed grant (Texas Tech University; to Z.N.K. and C.P.) and Texas Tech University Health Science Center start-up funds (Z.N.K.). E. Q. salary was partially supported by One Health Innovation seed grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Leishmania species cause leishmaniasis, a major health problem that affect around 12 million people worldwide [1] and is present in more than 98 countries [2]. The incidence of leishmaniasis is on the rise in new geographic areas in U.S. due to population growth, climate change, and increased travel [3]. While there is consensus that leishmaniasis is a uniquely tropical disease, evidence from recent studies indicate that 59% of cases are endemic in southern states including Texas, where it is prevalent and has a genotypically distinct Leishmania mexicana strain [4–6]. Control of leishmaniasis is hampered by the absence of a safe vaccine, inherent limitations of current medications, drug toxicity, and frequent treatment failures stemming from drug resistance and the pathogen’s persistence.
In general, persistence is defined as an adaptive phenotype where the organism exhibits a transient increase in tolerance to different stress conditions such as drugs, reactive oxygen species (ROS), pH, temperature, starvation or interaction with the host immune system [7]. Persisters are known to be present in bacteria [8], fungal pathogens [9] and protozoa parasites [10]. However, persister cells can also be found in cancer, leading to cancer relapse and drug resistance [11]. During this state the cell typically adopts a non-growing or slow growing state with reduced metabolism [8,12]; nevertheless, this phenotype is complex and heterogeneous, recent reports indicate that distinct subpopulations within persisters communities may retain the ability to replicate actively [13]. Persisters are capable to exit a non-dividing state and return to the proliferative state when an insult, such as drug, is gone. Therefore, drug persistence in pathogens and cancer cells represents a major clinical challenge. It contributes to treatment failure, drives the emergence of drug-resistant populations, and represents a serious global health threat [14]. Studying persister cells remains challenging due to their extreme rarity within a population, non-heritable phenotype, and dormant or slow-growing state. Consequently, standard in vitro enrichment approaches utilize lethal drug treatments such as high-dose bactericidal antibiotics or chemotherapy to kill most of the cells, leaving behind the tolerant persisters [15–20].
While resistance arises from genomic mutations that enable cells to survive and proliferate in the presence of drugs [10], persistence is a non-heritable phenotypic variation. Persister cells remain genetically identical to non-tolerant cells [8,21], and once the stressor is removed from the medium, those cells can resume their proliferation after certain time and exhibit sensitivity to the original stressor [12]. For this reason, compared to resistance, persistence is considered a transient and reversible stage [22]. However, this reversal to the susceptible phenotype cannot be immediate due to the presence of molecular memory of prior exposure to the stressor [23]. We define molecular memory as the ability of a cell to remember a past event or stimuli long after that event has ended, fundamentally priming the cell for altered future responses [24]. Molecular memory plays an imperative role in the development of drug tolerance/persistence. In persister cells, both epigenetic adaptations and epitranscriptomic RNA modifications help establish a memory of past stress, enabling rapid phenotypic adaptation and survival during recurring drug exposure [18,25,26]. The molecular memory or phenotype of drug-tolerant persister cells can be maintained for days to up to 4 weeks in bacteria after environmental stress or drug withdrawal, depending on the species and depth of dormancy, before the cells reactivate, clear internal damage, and become fully susceptible to drugs again [23,25].
The mechanisms associated with generation of persistence are poorly understood. There is an ongoing debate regarding whether cell dormancy and low metabolism are merely consequences of environmental stressors or if they are regulated mechanisms that actively drive the population into persister state. In bacteria, the dormant state has been associated with the activation of stress pathways, allowing dormant cells to survive because the drug cannot fulfill its function [27]. Furthermore, several specific mechanisms have been linked to bacterial persistence, including toxin-antitoxin (TA) modules, genes associated with metabolic regulation, inactivation of pathways targeted by antibiotics, proteins involved in heat shock response, trans-translation, the SOS response, the stringent response mediated by (p)ppGpp, efflux pumps, epigenetic modifications, RNA degradation, and small non-coding RNA modulating translation [12,27,28]. Recently, deficiency in translation has been identified as one of the important mechanisms leading to dormancy and persistence in bacteria [29,30]. Cancer persisters mirror the bacterial model, emphasizing non-genetic mechanisms and a state of reduced proliferation. The persister state in cancer cells is highly plastic and reversible, often involving epigenetic and epitranscriptomic modifications, as well as metabolic shifts that enhance survival under drug pressure [31]. These cells can exit the dormant state and resume proliferation once the drug pressure is removed, leading to tumor relapse. Cancer persisters don’t just stop protein production, they specifically boost the translation of mRNAs that promote survival and resistance [32]. Thus, in both bacterial and cancer persisters, the role of translation involves a global suppression of protein synthesis combined with the selective translation of specific pro-survival mRNAs to adapt to stress and evade treatments. This selective control allows cells to enter a dormant or slow-cycling state, rather than dying, and eventually re-emerge to cause chronic infections or cancer relapse.
Persisters have been reported in protozoan pathogens such as Plasmodium spp [33,34], Trypanosoma spp [35,36], Toxoplasma spp, and Leishmania spp [10,21,37]. This behavior is fundamental for parasites to establish persistent infections within different hosts and survive the environmental stress. For instance, during Leishmania infection in mammalian hosts, a population of persister parasites has been identified, represented as a small number of parasites that include both rapidly replicating and quiescent subpopulations [38]. Those parasites exhibit remarkable resistance to hostile conditions, such as oxidative stress from nitric oxide (NO) generated by host immune cells, which may also confer high tolerance to antileishmanial drugs.
While the mechanisms associated with drug resistance in Leishmania have been widely studied, ranging from a genetic component with mutations in key efflux transporters [39,40] and metabolic adaptations [41], to lipidome remodeling [42] and translational control [43], the role and underlying causes of persistence in this parasite remain unclear. To address this, the present study established a model to generate persister-like promastigotes of Leishmania mexicana and examined the transcriptomic changes associated with elevated drug tolerance and persister-like features.
Results
Experimental design to generate persister-like parasites
The study of persisters in Leishmania is hampered by the lack of suitable models and difficulties in their isolation. To address this, we have established conditions to generate persister-like parasites that enter a non-dividing or slow-growing state and exhibit enhanced drug tolerance. In summary, persister-like promastigotes were induced in vitro by using antimony drug at a concentration that killed 80% of parasites; surviving cells were purified on Ficoll gradient, resuspended in drug-free medium for recovery (12h), then examined for cell proliferation in drug-free medium and drug tolerance upon rechallenge. Our experimental design comprised four stages: a) parasite culturing, b) drug challenge and isolation of survived parasites on Ficoll gradient, c) recovery in drug-free medium and d) re-challenge with antimony drug (Fig 1).
The workflow comprises four stages (A-D): (A) Parasite culturing: L. mexicana promastigotes were seeded at 5x105 cells/mL and collected at the early log phase to ensure high parasite viability (> 95%). (B) Drug challenge: the collected parasites were split into two groups – 1) parental parasites or the control group, which was never treated with the drug, and 2) treated group, a parasite population that was challenged with the drug (EC80 dose: 0.315 mM PAT) to kill 80% of parasites. Parasites that survived the treatment considered to be persister-like. Both parental and treated live persister-like were separated from dead cells on Ficoll gradient to reduce technical variability. (C) Recovery: after Ficoll isolation live persister-like were twice washed to remove the drug remnant and recovered in a medium depleted of the drug. Similar treatment was applied to the control group. (D) Re-challenge: after recovery persister-like were re-exposed to a second challenge with varying PAT doses (2, 1, 0.5, 0.25, 0.125, 0 mM). Parental group was subjected to the same drug challenge first time as a control. The colorimetric MTT cell viability assay was used to quantify improved tolerance compared to the parental control. The estimated time required per stage is described in hours. Trivalent antimony was administered as potassium antimonyl tartrate (PAT). Cells were collected for RNA extraction from groups 1–3 for RNA-seq and subsequent transcriptomic studies. Created in BioRender. Quiceno, E. (2026) https://BioRender.com/chk24tl.
Initially, an MTT assay was performed on the parental strain exposed to varying PAT concentrations (0.125-2 mM) to determine the half-maximal effective concentration (EC50), defined as the drug concentration that reduces parasite viability by 50% (Fig 1A and S1 Fig). Based on the parental strain’s non-linear regression model (EC50 = 0.1638 mM, Hill slope = -2.114; S1 Fig), we calculated an EC80 of 0.315 mM. This concentration, which reduces the live parasite population to 20% (persister-like), was selected for all follow-up experiments because it yields enough cells for deep RNA-seq and drug tolerance studies.
Persister-like parasites exhibit lower proliferation rate and significantly higher tolerance to drug
After establishing the EC80, we cultured susceptible L. mexicana promastigotes for 48 hours before splitting them into two conditions. The “Induced persister-like” group received 0.315 mM PAT (EC80) for 24 hours, while the control group (“Parental”) remained untreated. After 24 hours of drug challenge, treated parasites displayed two different morphologies: apoptotic shape, characterized by rounded parasites; and regular slender morphology in live survivors, considered as persister-like promastigotes (S2 Fig). We isolated viable parasites (Induced persister-like and Parental control; Fig 1B) via Ficoll enrichment, obtaining aliquots for RNA extraction and growth curve assay (designated as T0 – time 0) (Fig 1B). The growth rate was monitored in drug-free medium by over 96 hours, with follow-up measurements every 12 hours (Fig 2A). Control samples reached a concentration peak of ~5.1x107 parasites/mL at hour 60, while treated samples showed a delayed proliferation reaching the peak of ~3.5x107 parasites/mL at hour 60 (Fig 2A). Moreover, treated parasites couldn’t reach the same density as parental cells, supporting the existence of cells in dormant-like state. Parasites were then washed and recovered in drug-free medium for 12 hours (Fig 1C). After this time, we collected cell aliquots and performed RNA extraction on “Recovery persister-like” and “Recovery parental” groups. Finally, we treated the “Recovery persister-like” and matching parental control (Recovery Parental) with 0.315 mM PAT (EC80) and performed RNA extractions on “Rechallenged persister-like” and “Parental + Drug” groups followed by RNA-seq for all acquired samples.
(A) The parasite proliferation was monitored during a recovery period of 96 hours by counting every 12 hours. Curves of growth show that persister parasites (red line) grow slower than the parental parasites (black line). Three independent biological replicates were measured. (B) The colometric MTT viability assay was used to measure the EC50 values for parental and persister-like parasites challenged with different drug concentrations in three biological replicates. Negative (-) or positive (+) symbols correspond to the presence/absence of drug. A representative experiment is shown. (C) EC50 values were calculated in three biological replicates using Graph prism and a Two-tailed T-test statistical method. Mean and standard error are plotted. The statistical difference was detected after comparing the EC50 values calculated for parental and persister parasites. value p < 0.0001. The raw data are available in Source Data 1 (S1 Data).
At the same time, we evaluated drug tolerance by exposing these recovered populations to 0.125–2 mM PAT to calculate new EC50/EC80 values in “Rechallenged Persister-like” (Fig 1D). Drug tolerance in parental and persisters-like parasites was measured using MTT viability assay (Figs 1D, 2B-C and S3 Fig). In the MTT assay, persister-like parasites reduced the yellow tetrazolium salt into a darker purple formazan product compared to parental parasites, which reflects their higher metabolic activity and increased viability measured by absorbance at 570 nm (Fig 2B and S3 Fig). The results revealed a significant increase in drug tolerance among the persister-like parasites, demonstrating roughly two-fold difference between the persister and parental groups (Fig 2C). Our findings indicate that persister-like parasites exhibit typical characteristics of persistence: enhanced tolerance and a quiescent state, followed by gradual resumption of growth.
Persister-like parasites exhibit extensive transcriptome remodelling
Three groups of RNA samples corresponding to induced persister-like promastigotes and parental control (group 1); recovery persister-like and recovery parental control (group 2) and rechallenged persister-like and parental + drug control exposed to the PAT for the first time (group 3) were selected for further analysis as described in our experimental design earlier (Fig 1). Each experimental condition was examined in three biological replicates, generating a total of 18 samples analyzed by deep RNA-seq.
Data were analyzed using the bioinformatic workflow described in S4 Fig. The produced reads were mapped against the Leishmania mexicana MHOM/GT/2001/U1103 genome of reference. Raw read counts per sample ranged from 25,521,644–32,949,665 reads. After QC, an average of 807,925 reads were discarded and the mean number of reads retained was 28,641,222. No sequences were flagged for poor quality by FastQC. Mean per-sequence and per-base accuracies were greater than 99.98% (Q > 38) before and after QC. Following alignment to the reference genome and gene-level quantification, an average of 23,918,544 (83.5%) of reads were assigned to annotated genes. After filtering out lowly expressed genes, an average of ~8,353 genes were considered for each group comparison. The number of significant DEGs (FDR < 0.05) for each comparison ranged from 2,136–5,419. These included 974–2,648 positively differentially expressed genes and 1,162–2,771 negatively differentially expressed genes per comparison.
To identify gene expression signatures associated with persistence we performed a bioinformatic analysis of high-throughput RNA sequencing based on five comparisons (Fig 3, Table 1).
Schematic representation defining six specific differential gene expression comparisons used to dissect the molecular mechanisms of persistence in Leishmania mexicana deep RNA-seq samples. This analysis facilitates the characterization of distinct biological states across the induction, recovery, and re-challenge phases. Comparison 1 (Induction Response); Comparison 2 (Resuscitation in Persister-like group); Comparison 3 (Resuscitation Phase in Persister-like vs parental control); Comparison 4 (Drug tolerance Memory); Comparison 5 (Drug response in persister-like parasites); Comparison 6 (Drug response in parental parasites).
Comparison 1 (Induced persister-like vs Parental) investigates the initial state of surviving persister cells relative to the untreated parent population. This analysis enables the characterization of stress response pathways and treatment-induced dormant states, establishing a baseline comparison to identify the immediate transcriptomic signatures of survival and stress tolerance under initial PAT pressure during formation of persister-like promastigotes.
Comparison 2 (Recovery persister-like vs Induced Persister-like) allows us to understand the molecular mechanisms that drive exit from the persistent state following drug removal. This analysis of the reactivation dynamics shows specific transcriptomic shifts required to exit metabolic quiescence and resume active proliferation.
Comparison 3 (Recovery persister-like vs Recovery Parental) identifies transcriptomic changes in persister-like parasites during recovery or resuscitation phase relative to control that was not exposed to drug (Recovery Parental). It characterizes the gene expression following drug withdrawal, assesses if persister-like parasites retain a molecular memory of prior treatment and evaluates whether surviving parasites remain different from the parental population once the stressor is removed and cells resume normal growth and increased metabolic activity.
Comparison 4 (Rechallenged persister-like vs Parental + Drug) allows the determination of drug tolerance features by investigating whether previously exposed parasites have a molecular memory of prior exposures and react differently to the drug pressure compared to a population facing it for the first time.
Comparison 5 (Rechallenged persister-like vs Recovery Persister-like) focuses on the molecular adaptations that occur when persister-like parasites, after recovery phase, are re-exposed to stressful drug-induced conditions. This comparison is essential for identifying the specific gene expression shifts that enable enhanced tolerance and quiescence.
Comparison 6 (Parental + Drug vs Recovery Parental) evaluates the transcriptomic response of sensitive parental parasites to drug stress. By comparing treated and untreated populations, this analysis aims to identify the molecular mechanisms driving the response of sensitive parasites to drug pressure. Further analysis of differences in drug responses in parental and rechallenged persister-like parasites (comparison 5 vs 6) allow to determine the essential transcriptomic signatures associated with reduced proliferation and drug tolerance.
Differential gene expression analysis revealed a reprogramming at the transcriptome level between persister-like and parental Leishmania populations across the evaluated conditions (Fig 4). Number of genes reported herein were selected based on a FDR transformed p < 0.05. Bioinformatic analysis of immediate transcriptomic changes in parasites that survived initial drug treatment compared to untreated parental parasites (Comparison 1; Induced persister-like vs Parental) revealed that a total of 3,124 genes were differentially expressed (Fig 4). Of those, 1,537 genes were downregulated, suggesting the supression of different biological processes in order to survive the stress caused by the drug. It is typically associated with a strategy where parasites reduce their metabolic activity and proliferation to face drug exposure [44]. Likewise, 1,587 genes were upregulated, which could be an indicator of activation of specific genes associated to drug stress response and involved in survival pathways that are required for the establishment of the persistent population [45]. Since Leishmania parasites have polycistronic transcription, the transcriptional control mechanisms are limited [46]. Therefore, the significant shifts in steady-state mRNA abundance likely reflect post-transcriptional mechanisms such as altered mRNA stability, trans-splicing efficiency, or translation-linked mRNA turnover leading to the remodeling of transcriptome.
Volcano plots showing the differential gene expression for the six specified experimental comparisons (Comparisons 1–6) based on statistical significance thresholds. The x-axis represents log2 fold change (log2FC), and the y-axis shows the statistical significance as the negative base-10 logarithm of the adjusted p-value (-log10 FDR). The horizontal dashed line indicates the statistical significance threshold (FDR < 0.05). Significantly upregulated genes are highlighted in red, while significantly downregulated genes are indicated in blue. Dots under the horizontal dashed threshold represent non-significant transcripts. Specific labeled data points correspond to key genes associated with the nucleolus. Each plot summarizes changes at the transcriptome level associated with the transition between parental and persister-like states. Comparisons 1–6 in raw data are designated as C1-C6. The raw data are available in Source Data 2 (S2 Data).
The direct comparison between Recovery Persister-like and Induced Persister-like (Comparison 2) provides insights into how the parasite exits dormancy following drug stress. This contrast defines a “transcriptomic recovery” phase, characterized by the significant upregulation of 2,371 genes and the downregulation of 2,524 genes. Crucially, the upregulated transcriptomic profile offers a direct window into the specific molecular pathways that the parasite must reactivate to successfully exit latency and resume cellular proliferation. Examination of induced persister-like cells in drug-free medium during recovery stage (Comparison 3; Recovery persister-like vs Recovery Parental) revealed that even after 12 hours of recovery in drug-free medium, transcriptome of persister-like populations was drastically different with 2,510 genes upregulated and 2,524 downregulated compared to parental parasites that were never treated with drug. Analysis of rechallenged persister-like and parental cells exposed to the drug first time (Comparison 4; Rechallenged persister-like vs Parental + Drug) demonstrated that 2,417 genes were upregulated and 2,429 genes were downregulated in rechallenged persister-like parasites. This differential gene expression indicates that recovered persister-like parasites respond to drug rechallenge in a drastically different manner compared to the parental population facing the drug for the first time. Our findings suggest that previous drug exposure alters the persister population in such a way that key survival genes remain differentially expressed even after drug removal. This persistent gene expression acts as a “molecular memory” that equips persisters to better handle subsequent drug exposure compared to drug-naïve parental cells.
Analysis of drug response in drug-tolerant parasites upon rechallenge compared to recovered persister-like (Comparison 5; Rechallenged persister-like vs Recovery Persister-like) reveals that fewer genes were differentially expressed, (980 Up vs 1,161 Down) again suggesting the presence of a molecular memory in the parasites that allow them to be pre-adapted to withstand drug exposure. Due to this, the rechallenge does not trigger a large remodeling at the transcriptome level, because some key survival pathways could remain active. This provides additional support for the presence of a molecular memory in the persister-like populations.
Comparison 6 (Parental + Drug vs Recovery Parental) analyzed the response of drug naïve parasites upon initial exposure. In contrast to Comparison 5, this comparison captures the immediate, acute response of drug-naïve parasites facing drug stress reflecting a primary stress response characterized by a high number of differentially expressed transcripts with 2,658 transcripts upregulated and 2,779 downregulated. When analysed alongside comparison 4, we observed drastic differences in transcriptomes between naïve and previously exposed populations.
Across comparisons, persister-like parasites displayed a substantial number of differentially expressed transcripts, indicating a response to drug-induced stress. Notably, both upregulated and downregulated gene sets were observed, suggesting that persistence is associated with coordinated activation and repression of distinct biological pathways rather than a unidirectional transcriptome shift. These results support a global remodeling of the transcriptome during the transition to the persister-like state.
Given the global remodelling of the transcriptomes observed across all the comparisons, we next sought to determine whether specific functional gene categories were preferentially affected in drug-tolerant parasites. Because the nucleolus is known to drive transcriptome remodeling by coordinating ribosome biogenesis and acting as a stress sensor [47], we analyzed changes in the expression of nucleolar genes. Bioinformatic analysis revealed differential expression for many nucleolar genes across all tested conditions as shown on volcano plots, supporting an altered ribosome biogenesis and importance of translational control in the formation persister-like populations (Fig 4). This control may directly influence mRNA stability by enabling selective translation; during stress, specific stress-response transcripts evade degradation and are actively translated [48]. Conversely, general transcripts are repressed and targeted for subsequent degradation through the interaction of RNA-binding proteins with their 3’ untranslated regions [49].
In many organisms, stress conditions promote selective translation through remodeling of ribosomal protein composition [50,51]. To further understand whether the persister-like state involves modulation of the translational machinery components, we focused on transcripts encoding ribosomal proteins (RPs) (Fig 5). Differential expression analysis revealed a distinct and non-uniform pattern of RP transcript regulation in persister-like parasites compared to parental controls. Unlike the global gene expression trends, RP transcripts exhibited a more nuanced distribution, with specific subsets of paralogs being selectively upregulated while others were downregulated during induction phase (Induced persister-like vs Parental). This heterogeneous pattern suggests a potential reconfiguration of ribosomal composition rather than a simple reduction or increase in ribosome biogenesis, which could be associated with the formation of specialized ribosomes that contribute to translational control under stress conditions. Further remodeling of protein synthesis machinery was observed in persister-like parasites upon drug removal, with 90 ribosomal protein transcripts upregulated and only 2 downregulated (Recovery persister-like vs recovery parental). Interestingly, when persister-like parasites were rechallenged with the drug for second time, they exhibited rather a drastic downregulation (22 transcripts downregulated vs 2 transcripts upregulated) of protein synthesis machinery compared to the induction phase (Induced persister-like vs Parental) and parental parasites (Parental + drug vs Recovery Parental). This downregulation coincided with the increase in the drug tolerance observed upon rechallenge (Fig 2).
Volcano plots showing differential expression of genes encoding ribosomal proteins (RPs) between the six evaluated comparisons (Comparisons 1–6). The x-axis represents log2 fold change (log2FC), and the y-axis shows the statistical significance as the negative base-10 logarithm of the adjusted p-value (-log10 FDR). The horizontal dashed line indicates the statistical significance threshold (FDR < 0.05). Significantly upregulated genes are highlighted in red, while significantly downregulated genes are indicated in blue. Dots under the horizontal dashed threshold represent non-significant transcripts. The distribution highlights selective regulation of specific RP paralogs, suggesting potential remodeling of ribosomal composition. 163 genes encoding RPs were evaluated. The raw data are available in Source Data 3 (S3 Data).
Overrepresentation analysis uncovers multifaceted adaptations and unique pathway expression signatures in persister-like parasites
To determine coordinated functional trends and pathways in persister-like parasites, we performed Overrepresentation Analysis (ORA) across all the comparissons (Fig 6). Normalized Enrichment Scores (NES) demonstrated consistent enrichment patterns across Biological Process (BP), Cellular Component (CC), and Molecular Function (MF) ontologies. The enrichment patterns revealed a consistent overrepresentation of pathways linked to ribosome biogenesis, rRNA processing, and nucleolar function, particularly in comparisons involving the induction and recovery phases. In general, the acute phases of drug response (Induced persister-like and Parental + Drug) are characterized by a profound global shutdown of multiple biological processes. This widespread downregulation might serve as a primary survival mechanism to withstand drug toxicity, being notably more pronounced in the induced persister-like population, which reflects a highly specific and coordinated stress response. Conversely, the transcriptomic landscape shifts during the recovery phases (Comparisons 2 and 3). Upon transfer to a drug-free medium, we observed a broad upregulation across numerous pathways such as an increase in ribosome production, cellular biosynthesis and mitochondrial function. This transcriptional reversal clearly captures the parasites exiting dormancy, representing an active biological effort to reactivate metabolism and resume normal growth dynamics. Meanwhile, comparisons evaluating the second drug response in rechallenged persister-like parasites (Rechallenged Persister-like vs. Recovery Persister-like, and Rechallenged Persister-like vs. Parental + Drug) demonstrate a rapid return to metabolic quiescence. Upon re-exposure to the stressor, these parasites exhibited a severe downregulation of ribosomal pathways alongside a broad suppression of core regulatory networks, including general gene expression, macromolecule biosynthesis, and global metabolic processes. This indicates a primed, highly efficient repression of energy-intensive anabolic pathways to ensure survival during recurring stress. These findings are in agreement with the differential expression patterns observed for ribosomal proteins transcripts (Fig 5), reinforcing the central role of translational regulation in the persister-like phenotype.
Overrepresentation Analysis (ORA) comparing all the evaluated groups. Cells show the Normalized Enrichment Score (NES) for Gene Ontology (GO) terms associated with Biological Processes (BP), Cellular Components (CC), and Molecular Functions (MF). Cells are differentially colored based on wheter they were significantly upregualted or down regulated among the evaluated groups. GO terms were annotated based on their association to ribosomal or nucleolus processes. Degree of significance within cells is denoted by the following p-value thresholds. * = p < 0.05; ** = p < 0.01; *** = p < 0.001. The raw data are available in Source Data 2 (S2 Data).
Thus, our results indicate that the transcriptome remodeling observed in persister-like parasites is functionally coherent and strongly biased toward processes involved in protein synthesis and ribosome dynamics to coordinate multifaceted changes in metabolism and other processes.
Persister-like parasites exhibit distinct drug response profiles driven by the downregulation of protein synthesis machinery and upregulation of drug-response genes
To determine differences in drug responses of drug-tolerant populations, we compared rechallenged persister-like parasites with drug-naïve parental parasites exposed to the treament for the first time (Comparison 4 vs Comparison 5, Fig 7). The Venn diagram (Fig 7A) illustrates both shared and unique sets of differentialy expressed genes between conditions, highlighting that rechallenged parasites exhibit a distinct response. Our bioinformatic analysis revealed that 241 (197 + 44) transcripts are uniquely upregulated only in rechallenged persister-like parasites versus parental control treated with drug for the first time (Fig 7A). Among them, 44 upregulated transcripts were downregulated in treated parental control. Similarly, rechallenged persister-like exhibited 316 (255 + 61) uniquely downregulated transcripts, 61 of which were upregulated in control parasites upon drug exposure. Thus, we identified unique differences in drug responses in rechallenged persister-like that could contribute to the increased drug tolerance. Interestingly, drug-naïve parental parasites responded to the drug in a broader way with 1,858 transcripts uniquely upregulated and 1,890 being downregulated. This difference in the scale of uniquely upregulated (241 versus 1919) or downregulated (316 versus 1934) transcripts indicates a coordinated and targeted response in drug-tolerant parasites. This shift suggests that rechallenged parasites retain a molecular memory of prior drug exposure. The shared response included the upregulation of 739 transcripts and the downregulation of 845 transcripts in both rechallenged and drug-naïve parental parasites.
(A) Venn diagram comparing upregulated and downregulated genes among the groups ‘Parental + Drug’ and ‘Rechallenged persister-like’. Percentages are calculated based on the total number of genes significantly upregulated and downregulated among the two groups [5],889). (B) Biological pathways associated with uniquely upregulated (197 + 44) or downregulated (255 + 61) genes in rechallenged persister-like parasites in response to drug. Hypothetical genes without known function were removed from the analysis (255 genes). The plot highlights major biological processes altered in persister-like parasites during response. The translation term is the result of the combining of different GO terms, all of which are related to the translation machinery (GO:0006412, GO:0003743, GO:0006414, GO:0003735, GO:0005840, GO:0003723) (C). Biological processes enriched among uniquely downregulated genes in rechallenged persister-like group (255). Significant pathways associated with quiescence state were detected in this set of genes. The raw data are available in Source Data 4 (S4 Data).
Further analysis of the unique responses in rechallenged parasites demonstrated that these genes are associated with diverse biological processes involved in the parasite’s response to the drug pressure (Fig 7B). Among these pathways, the downregulation of translation and mitochondrial function stands out, which is consistent with the previous results observed in the ORA analysis. Other relevant pathways included the enriched response to drugs and xenobiotic factors, as well as the response to oxidative and osmotic stresses.
The functional enrichment analysis of uniquely downregulated genes (Fig 7C) showed that these genes are represented in significant biological processes associated with translation, ribosome composition and mithochondrial matrix, indicating the importance of translational control and repressing processes potentially linked to growth and proliferation, leading to dormancy in parasites and suggesting a specialized response to drug exposure.
Among the genes differentially expressed in rechallenged parasites in response to drug compared to first-time treated parental parasites, we prioritized those involved in drug response pathways due to their critical role in stress adaptation (Fig 7A). We then manually curated this subset to identify genes that discriminate drug-tolerant parasites from drug-sensitive parasites during response to the drug (Table 2).
Interestingly, we found that genes associated with drug uptake, such as aquaglyceroporins (AQPs), were uniquely downregulated in persister-like parasites. In contrast, efflux pumps, including ATP binding cassette (ABC) transporters, were upregulated, presumably to remove the antimony drug from drug-tolerant cells. These findings support the presence of a molecular memory in persisters-like parasites that allows a differentiated and efficient response to drug exposure.
Discussion
This study establishes a model to investigate the formation of drug-tolerant persister-like parasites and the molecular mechanisms of persistence in Leishmania mexicana. Our findings demonstrate that persister-like parasites undergo coordinated, dynamic gene expression remodeling to promote drug tolerance, suppress proliferation, and activate adaptive stress responses.
By utilizing Ficoll gradient centrifugation following exposure to a lethal dose of potassium antimonyl tartrate (PAT) that kills 80% of parasites, we successfully isolated the viable subpopulation of cells from the dead cells [44]. Phenotypic characterization of this enriched fraction confirmed the hallmark traits of persistence: a significant growth delay (lag phase) upon transfer to drug-free medium and a markedly increased tolerance to subsequent PAT exposure (Fig 2, S1 Data). Crucially, the ability of these parasites to eventually resume growth underscores the transient, non-heritable nature of this phenotype. Furthermore, these physiological adaptations are directly reflected in our bioinformatic analysis (Fig 3).
To understand the molecular mechanisms driving the persister-like phenotype, we designed a comparative bioinformatic strategy encompassing six specific biological comparisons. This structured approach allowed us to isolate the transcriptomic signatures of distinct survival phases: the immediate stress response under initial drug pressure (Comparison 1), the exit of dormancy stage (Comparison 2), the metabolic signature governing the delayed phase during recovery (Comparison 3), the transcriptomic “memory” by evaluating whether previously exposed parasites have differences in transcriptomes upon a subsequent drug challenge compared to a naïve population (Comparison 4), the molecular adaptations that occur when persister-like parasites, after recovery phase, are re-exposed to stressful drug-induced conditions (Comparison 5), and finally, examination of the transcriptomic response of non-persisters, sensitive parental parasites exposed to drug stress (Comparison 6). During the acute stress phases (Comparisons 1 and 6), parasites entered a state of targeted metabolic quiescence, defined by a drastic suppression of many biological pathways coupled with nucleolar hyperactivity, probably needed to remodel transcriptome and the mobilization of primary defense effectors, such as heavy metal transporters (Fig 4, S2 Data). Importantly, we demonstrated that this dormant state is fully reversible; upon drug removal (Comparisons 2 and 3), persister-like parasites exhibited a synchronous transcriptomic recovery, rapidly downregulating acute stress responses while reigniting ribosomal biogenesis to resume active proliferation. However, the true importance of this phenotype was revealed during the second drug challenge (Comparisons 4 and 5). Rechallenged persister-like parasites mounted a highly efficient, pre-adapted response that functionally phenocopies stable genetic resistance. Together, these stage-specific signatures demonstrate that Leishmania persistence is a highly plastic, actively regulated cycle of latency and transcriptomic memory, serving as a critical evolutionary bridge between transient survival and permanent drug resistance.
The extent of the transcriptomic remodeling is visually reflected in our global differential expression profiles. Volcano plots show the shifts in gene expression across all evaluated groups (Fig 4). Detailed analysis of these shifts revealed the significant upregulation of several gene families classically associated with stress adaptation and drug survival. For instance, specific phosphatases (LmxM.18.0440, LmxM.13.1510, and LmxM.31.0640) were highly upregulated in induced persister-like parasites (S2 Data). In Leishmania, the overexpression of phosphatases is frequently observed under stress conditions and in drug-resistant strains, likely supporting the increased energy demands of drug efflux mediated by ABC transporters [55]. Additionally, we observe the significant upregulation of key genes associated with the antimonial stress response, notably a putative zinc transporter 3 (LmxM.28.1930) and mitogen-activated protein kinase 2 (LmxM.13.0440) [52]. The induction of these specific transporters and kinases highlights their critical role in mitigating heavy metal toxicity and orchestrating early survival signaling pathways. Additionally, we observed a pronounced upregulation of multiple amastin surface glycoproteins (LmxM.08.0840, LmxM.08.0850, LmxM.24.1270, and LmxM.30.4052), primarily in the “Induced Persister-like” group. Amastins are critical for surface remodeling during environmental adaptation [43] and have also been found to be upregulated in parasites that were resistant to antimonials [52]. Interestingly, a subset of these genes (LmxM.08.0750, LmxM.30.0452) remained upregulated in the “Rechallenged Persister-like” (Comparison 5, S2 Data), suggesting that maintaining this surface modification might be a crucial defense mechanism when parasites re-encounter stressors. Conversely, during the drug-free recovery phase (Recovery Persister-like vs. Recovery Parental), amastin expression was generally downregulated, reflecting a dynamic reversion to a baseline physiological state once the stress is removed. Notably, the induction of persistence was accompanied by the upregulation of specific heat shock proteins, such as HSP70 isoform (LmxM.28.2780). The accumulation of HSPs is a well-documented hallmark of cellular stress and has been directly linked to antimonial resistance in Leishmania [56]
In Leishmania, drug stress induced by antimonials is known to trigger severe oxidative stress response, which alters the expression of genes associated with oxidoreductase activity, such as Cytochrome b5 and Cytochrome P450 [57]. Consistent with this, our analysis revealed an acute response to the initial drug stressor. We found that the cytochrome b5 Heme/Steroid binding domain containing protein (LmxM.30.1170) and cytochrome p450 (LmxM.29.3550) were significantly upregulated in both the Induced persister-like (Comparison 1, S2 Data) and the Parental + Drug groups (Comparison 6, S2 Data). Additionally, other genes associated with oxidoreductase activity (LmxM.33.3330 and LmxM.34.2560) were upregulated in Comparison 6, while a distinct set of b5 associated genes (LmxM.09.1500 and LmxM.30.1190) were uniquely upregulated in Induced persister-like parasites. This transcriptional shift highlights the role of specific oxidoreductase activity in the initial metabolic response and drug detoxification.
Conversely, this acute activation of stress responses is coupled with a targeted metabolic shutdown. We observed a marked downregulation of other cytochrome b5-associated genes, such as LmxM.26.1430, across the induction (Comparison 1) and tolerance memory phases (Comparison 4), as well as LmxM.30.1200 in the Comparison 6 group (Fig 4, S2 Data). Since certain cytochrome b5 networks are intrinsically linked to energy-demanding anabolic pathways like lipid elongation [58], this repression likely represents a survival strategy where the parasite shuts down high-energy consumption processes to endure drug toxicity. Interestingly, during the recovery period (Comparison 3), many of the previously upregulated P450 and b5 genes returned to basal levels, indicating that their activation is a direct and exclusive response to the presence of the drug. Furthermore, these pathways remained generally downregulated in the rechallenged persister-like group, suggesting a process of transcriptomic adaptation where the parasites have consolidated a tolerant state that no longer relies on this acute oxidoreductase route. This metabolic efficiency is further evidenced by the dynamic regulation of drug transporters during the drug-free recovery phases (Comparisons 2 and 3). By shutting down processes that are no longer necessary, the parasite conserves vital resources. A prime example is the putative zinc transporter 3 (LmxM.28.1930); while this gene was highly upregulated during the acute drug responses (Comparisons 1 and 6), it was significantly downregulated in both recovery comparisons. This rapid reversion illustrates a highly coordinated transcriptomic mechanism that swiftly deactivates energy-intensive detoxification routes once the stressor is removed.
In bacteria and cancer persisters, proteins involved in both epigenetic adaptations and epitranscriptomic RNA modifications begin to emerge as players that help establish a molecular “memory” of past stress, enabling rapid phenotypic adaptation and survival during recurring drug exposure [18,25,26,59]. However, identifying such players in Leishmania parasites remain extremely challenging since many canonical DNA and RNA modifying enzymes are not present in Leishmania genome. Moreover, identification of Leishmania specific players is complex, and many genes and proteins have not been fully annotated. For example, the mammalian writer enzyme METTL3 deposits m6A RNA modifications onto 5’-UTRs of specific survival and stress-response transcripts, promoting their upregulation and driving molecular memory in cancer persisters [60]. While the gene encoding METTL3 has not yet been identified in Leishmania, uncovering the mechanisms governing such epigenetic and epitranscriptomic adaptations is crucial for identifying vulnerabilities in parasite persisters. To address this, our comparison of the Rechallenged Persister-like group against the Parental + Drug group unveils potential players involved in molecular memory in parasites previously exposed to PAT (S2 Data). This pre-adapted state is characterized by upregulation of several modulators, mainly observed in rechallenged persister-like parasites. Specifically, it includes genes LmxM.20.0030 and LmxM.32.1790 encoding proteins like histone-lysine N-methyltransferases which belong to the family of histone methyltransferases, enzymes that are required for cell cycle regulation, maintenance of genome integrity in response to DNA damage, and initiation of replication [61,62]. The upregulation of this gene suggests that persister-like parasites may deploy targeted histone post-translational modifications to face the drug presence through epigenetic modifications [63]. Additionally, other modulators might be contributing to this post-transcriptional memory response. For instance, protein arginine N-methyltransferase 7 (PRMT7, LmxM.06.0870) which in Leishmania is associated with cytoplasmic activity and the methylation of RNA-binding proteins (RBPs) [64,65] could potentially contribute to selectively stabilizing mRNAs critical for the drug response. This hypothesis is further supported by the concurrent upregulation of a staphylococcal nuclease homologue/Tudor domain-containing protein (LmxM.31.0950), which might act as a reader to interpret the modifications generated by arginine methylation [66]. Furthermore, the upregulation of a putative RNA cytidine acetyltransferase (LmxM.17.1250) points to the possibility of direct epitranscriptomic RNA modifications, which could also play an adaptive role in the parasite’s response to the stressor [67]. Interestingly, this remodeling appears functionally linked to the metabolic requirements of the quiescent state. Rechallenged persister-like group upregulated a distinct suite of genes driving nutrient scavenging and catabolism. This includes Cathepsin L-like proteases for protein degradation (LmxM.08.1060, LmxM.08.1070), a lysophospholipase for lipid recycling (LmxM.24.1840), and a glutamine aminotransferase for amino acid utilization (LmxM.32.1330). Previous studies underscore a regulatory axis between these processes, demonstrating that the genetic ablation of specific methyltransferases directly disrupts the expression profile of catabolic enzymes such as LmxM.08.1070 and LmxM.32.1330 [68]. Thus, our data support the hypothesis that epigenetic/epitranscriptomic mechanisms likely orchestrate the metabolic shift required to sustain parasite viability during periods of translational dormancy and extreme environmental stress, which represents a promising avenue for future investigation.
Finally, to counteract the severe oxidative burst typically induced by antimonial drugs, rechallenged persisters mounted a rapid antioxidant response (S2 Data). This was evident by the targeted upregulation of key redox effectors, including a glutaredoxin-like protein (LmxM.27.0810) and thioredoxin (LmxM.01.0270), response that was unique for rechallenged persister-like parasites. Acting as non-enzymatic scavengers of free radicals, these proteins play a crucial role in preserving intracellular redox homeostasis, forming an essential defense layer that prevents lethal oxidative damage while the parasite remains in its non-replicative survival state [69,70].
The results of our Overrepressentation Analysis (ORA) reveal that translational control is a fundamental driver of the persistent-like features in L. mexicana (Fig 6), highlighting a profound alteration in the pathways associated with the nucleolus and ribosomal proteins. This massive restructuring of the translation machinery during induction stage (Fig 5, S3 Data) supports the need to remodel the translatome that is followed by the global reduction of protein synthesis machinery; additionally, our data support that downregulation of protein synthesis machinery is an essential adaptive strategy to slow growth down, allowing them to tolerate drug-induced stress. Interestingly, this translational and metabolic repression is not unique to protozoa but represents a universally conserved survival strategy in various domains of life [8,71,72].
Our transcriptomic data supports the hypothesis that Leishmania employs a convergent evolutionary strategy to face PAT pressure. By dynamically modulating the composition of the nucleolus, responsible for ribosome biogenesis, and subsequently altering the abundance of specific ribosomal subunits, drug-tolerant parasites are likely to minimize the drug’s toxic effects on active metabolism. At the same time, this translational pause allows them to conserve energy and reallocate resources exclusively towards basic cellular maintenance, by selectively and preferentially translating key transcripts involved in stress response and survival, thereby preserving their viability until the environmental stress subsides and they can resume proliferation [10,13]. Furthermore, this nucleolar enrichment could reflect a protective sequestration mechanism associated with the nucleolus detention pathway. During severe xenobiotic stress, eukaryotic cells frequently utilize the nucleolus to dynamically sequester essential cell cycle and metabolic proteins, safeguarding them from drug-induced damage or aggregation until the stress is removed [73,74].
Analysis of drug response genes in rechallenged persister-like parasites identified several genes that were uniquely altered in response to antimonial drug, differentiating drug-tolerant parasites from drug-naïve parasites and supporting presence of a regulated process required to survive drug pressure (Table 2, S4 Data). In Leishmania, the downregulation in the expression of membrane channels such as aquaglyceroporin 1 (AQP1, LmxM.30.0020) and aquaporin (LmxM.22.1420), alongside mitogen-activated protein kinases (MAPKs, LmxM.24.2320), is known to drive drug resistance by reducing cellular drug uptake [39]. Likewise, resistance mediated by the overexpression of ABC transporters (LmxM.23.0250, LmxM.23.0220) and P-Glycoprotein (LmxM.30.1270), are key for drug efflux [39]. The trend observed in the persister-like population suggests that these parasites might be using a similar coordinated transport mechanism to survive acute drug pressure.
Any cellular adaptation to an adverse environment can be conceptualized as having two distinct components: a general stress response and a stressor-specific response. In Leishmania, the general stress response characterized by metabolic restriction and a slowed growth rate shares partial programmatic overlap with normal developmental differentiation into amastigotes [75–78]. However, the phenotypes observed in our study also exhibit a distinct, stressor-specific response unique to trivalent antimony exposure. This specific component includes the targeted disruption of thiol redox metabolism, the regulation of aquaglyceroporin drug influx channels, and the activation of ATP-binding cassette (ABC) efflux pathways (Table 2, S4 Data). Therefore, while drug pressure may partially trigger general pathways that mimic differentiation, the resulting drug-tolerant or persister-like state is uniquely defined by this specific antimony-defense machinery.
Altogether, these findings demonstrate that drug persistence in Leishmania is a highly sophisticated, active survival strategy, as is summarized in Fig 8. This transient phenotype is orchestrated through precise transcriptome remodeling, coordinated translational downregulation, and distinct adaptations. Moreover, this state likely represents the initial evolutionary steppingstone toward the acquisition of permanent genetic drug resistance, as previously described in bacterial models [79]. By establishing the model to study persister-like features in this protozoan parasite, we could define the survival mechanism of Leishmania when facing drug pressure, while also highlighting novel molecular vulnerabilities within the persister-like population, offering promising therapeutic targets to intercept the transition to resistance and ultimately prevent clinical relapse.
Following a drug challenge, surviving persister-like parasites remodel their transcription through a general downregulation of structural and metabolic pathways, entering a dormant-like state. Upon drug removal, recovered parasites upregulate macromolecular biogenesis, alongside energetic and metabolic processes, to exit dormancy and resume proliferation. When subjected to a second drug challenge, rechallenged persister-like parasites repress drug uptake, metabolism, and protein synthesis while activating drug efflux leading to enhanced drug tolerance. This dual mechanism results in a rapid, primed response, efficient metabolic quiescence, and enhanced survival under recurrent drug pressure. Created in BioRender. Rodriguez, C. (2026) https://BioRender.com/dl3imnc.
Study limitations and future directions
Understanding molecular mechanisms of persistence is still at infancy due to the lack of suitable in vivo models and difficulties to isolate persisters. While our study provides a high-resolution, time-resolved look at the potential mechanisms underlying transient drug survival, several key limitations must be acknowledged. Our experimental model utilizes axenic persister-like promastigotes in vitro. While promastigotes offer a valuable, clean system for isolating baseline protozoan stress responses to the drug treatment, true persistent Leishmania infection clinically manifests within the mammalian host, specifically inside macrophages as intracellular amastigotes. The complex host-parasite tissue environment, immune pressures, and the unique metabolic constraints of the phagolysosome are not captured by an axenic culture system. Future studies are essential to translate these findings into host intracellular amastigote models and in vivo infection systems, which will determine if these specific transcriptomic survival pathways are conserved during clinical persistence. Our transcriptomic findings strongly support protein synthesis remodeling/downregulation that will be validated through comprehensive proteomics and polysome profiling in future studies. Furthermore, while this study evaluated drug responses at a single time point, future work will utilize time-course experiments to track early and late cellular changes in persister-like parasites.
Materials and methods
Parasite culturing
Promastigotes of Leishmania mexicana (MNYC/BZ/62/M379 GFP) were cultured at 26°C in complete M199 medium (M199 medium at pH 7.4 supplemented with 10% fetal bovine serum, 1.82 g/L sodium bicarbonate (NaHCO3), HEPES 40 mM (pH 7.4), 0.1 mM adenine, 1 mg/L biotin, 5 mg/L hemin, 2 mg/L biopterin 100 units/mL of penicillin and 100 μg/mL of streptomycin) [17]. Parasites were seeded at 5x105 cells/mL and incubated for 48 hours until reaching early log phase. Promastigotes in this phase were harvested and seeded to required concentration for each experiment henceforth.
Estimation of EC50 and EC80 doses
Early log phase promastigotes were harvested and seeded at 2x108 cells/mL in Schneider’s insect medium (Sigma, Cat: S0146) containing 10% FBS (Corning, Cat: 35–011-CV) and 1% Penicilin-Streptomycin (Sigma-Aldrich, Cat: P0781). Half maximal effective concentration (EC50) of parasites treated with potassium antimony (III) tartrate (PAT, Sigma-Aldrich, Cat: 230057) was estimated by MTT assay in 96-well plates under two experimental conditions: control samples with 50 µL of parasite culture combined with 50 µL of Schneider’s medium without drug, and treated samples including 50 µL of parasite culture combined with 50 µL of Schneider’s medium with serial dilutions of PAT (2, 1, 0.5, 0.25, 0.125 and 0 mM). Schneider’s medium without drug was used as blank. Three biological repeats each with three technical repeats were analyzed. Plate was incubated at 26°C for 24 hours, after drug challenge, 20 µL of MTT solution at 5 mg/mL (Sigma-Aldrich, Cat: M2128) were added to each well to evaluate parasite viability. After four hours treatment with MTT, reaction was stopped by adding 100 µL of 10% SDS-HCl solution and absorbance was measured at 570 nm. EC50 value was calculated by non-linear regression analysis on dose-response curve using GraphPad Prism 9. EC80 was estimated with EC50 value and the slope factor using online GraphPad calculator (https://www.graphpad.com/quickcalcs/ECanything1/) [43].
EC80 drug treatment
After 48 hours incubation under previously described conditions, early log phase culture was harvested and seeded at 2x108 cells/mL in complete M199 medium with or without PAT according to the group: Control group included parasites without drug, while treated group was composed of parasites challenged with PAT at 0.315 mM, which is the concentration that kills 80% of parasite population (EC80). The cultures were incubated at 26°C for 24 hours and each group was analyzed in triplicates.
Live parasite separation on ficoll gradients
Once the treatment finished, Leishmania cells with good viability were enriched with Ficoll gradients. Briefly, promastigotes were harvested and centrifuged at 3,000 × g for 10 min. The pellet was resuspended in 4 mL of PBS. The suspension of cells was added as the last layer on a Ficoll gradient (Ficoll Type 400) prepared in 15 mL tubes containing 2 mL of Ficoll 20% at the bottom and 2 mL of Ficoll 10% on top. Gradients were centrifuged at 1,300 × g for 15 min at room temperature. After centrifugation, the fraction of Ficoll 10% containing live cells was recovered and washed with 10 mL of M199 medium. After centrifugation at 2,500 × g for 5 min, the pellet was resuspended in 1 mL of medium M199. Cell concentration was calculated, 2 × 107 cells were centrifuged, and the pellet was resuspended in TRIzol and stored at -80°C until RNA purification. An aliquot of each replica was taken to check proliferation; these aliquots were considered as T0. An additional aliquot of each replica was used to examine resuscitation for 12 hours in drug free medium followed by cell collection for RNA extraction.
Determination of parasites tolerance to drug
To test the tolerance to different drug concentrations, an aliquot was taken after 12 hours of recovery after induction phase. The number of cells was estimated, and parasites were seeded in 96-well plates at 5x106 motile cells/mL in Schneider’s insect medium containing different known concentrations of PAT (2, 1, 0.5, 0.25, 0.125 mM) by triplicate. Plate was incubated for 24 hours at 26°C, then 20 µL of MTT 5 mg/mL were added per well to evaluate the parasite viability. After four hours of MTT treatment, the reaction was stopped by adding 100 µL of 10% SDS-HCl solution and absorbance was read at 570 nm. EC50 values were estimated using non-linear regression.
Parasites proliferation after drug treatment
After EC80 drug treatment live parasites enriched on Ficoll gradients were collected and aliquot of each replica corresponding to t = 0, including both control and treatment, was centrifuged at 2,500 x g for 5 minutes and washed twice with M199 medium to remove all drug remnant. Then, each culture was seeded at 2.5x105 cells/mL in fresh M199 media without any drug for parasites recovery. Cultures were incubated at 26°C for 96 hours. Growth rate was monitored during parasite recovery by taking aliquots from each sample and counting by hematocytometer every 12 hours.
RNA purification
RNA was isolated using a modified protocol from Invitrogen TRIzol LS protocol. 0.75 mL of TRIzol LS reagent was added per 0.25 mL of sample. Samples were homogenized by pipetting and incubated for 5 min to ensure a complete lysis. 0.2 mL of chloroform were added per 0.75 mL of TRIzol LS, the samples were incubated 3 min and centrifuged for 15 minutes at 12,000 × g at 4°C. After that the aqueous phase was recovered into a new tube and 1 µL of glycogen and 0.5 mL of isopropanol per 0.75 mL of TRIzol were added. Samples were incubated overnight at -20°C, washed with ethanol and dried RNA pellet was resuspended in 30 µL of nuclease-free water. Each sample was quantified by NanoDrop One (Thermo Scientific).
High-throughput sequencing
Eighteen samples were processed for high-throughput sequencing of RNA (Deep RNA-Seq). Isolated RNA sample quality was assessed by High Sensitivity RNA Tapestation (Agilent Technologies Inc., California, USA) and quantified by AccuBlue Broad Range RNA Quantitation assay (Biotium, California, USA). Paramagnetic beads coupled with oligo d(T)25 were combined with total RNA to isolate poly(A)+ transcripts based on NEBNext Poly(A) mRNA Magnetic Isolation Module manual (New England BioLabs Inc., Massachusetts, USA). Prior to first strand synthesis, samples are randomly primed (5´ d(N6) 3´ [N = A, C, G, T]) and fragmented based on manufacturer’s recommendations. The first strand is synthesized with the Protoscript II Reverse Transcriptase with a longer extension period, approximately 40 minutes at 42ºC. All remaining steps for library construction were used according to the NEBNext Ultra II Non-Directional RNA Library Prep Kit for Illumina (New England BioLabs Inc., Massachusetts, USA). Final libraries quantity was assessed by Qubit 2.0 (ThermoFisher, Massachusetts, USA) and quality was assessed by TapeStation D1000 ScreenTape (Agilent Technologies Inc., California, USA). Final library size was about 430 bp with an insert size of about 300 bp. Illumina 8-nt dual-indices were used. Equimolar pooling of libraries was performed based on QC values and sequenced on an Illumina NovaseqX plus platform (Illumina, California, USA) with a read length configuration of 150 PE for 40M PE reads per sample (20M in each direction).
Data preparation for RNA-seq analysis
FASTA, GFF, and GAF files for Leishmania mexicana reference strain MHOM/GT/2001/U1103 were downloaded from TriTrypDB v68 [80]. The GFF file was converted to GTF, using the tool agat_convert_sp_gff2gtf.pl [81] to meet downstream requirements. fastp v1.0.1 [82] was used to process the paired raw FASTQ files, and arguments --trim_poly_g and --trim_poly_x were explicitly specified to trim polyG sequencing artifacts and polyA mRNA tails. FastQC v0.11.8 [83] was used to assess quality pre and post processing. Reports were aggregated using MulitQC v1.29 [84] STAR v2.5.2b [85] and used to index the reference genome and align clean reads. Aligned reads were subsequently quantified using featureCounts v2.1.1 [86]. A custom L. mexicana annotation database was built using makeOrgPackage from the AnnotationForge package [87] in R v4.2.1 based on curated Gene Ontology (Biological Process, Cellular Component, and Molecular Function) annotations.
Differential expression analysis and functional enrichment analysis
Differential expression was assessed using the edgeR (v3.38.4) quasi-likelihood analysis pipeline [88] in R v4.2.1. Lowly expressed genes were excluded using filterByExpr [88]. Library sizes were normalized with the trimmed mean of M values (TMM) method using calcNormFactors [88]. Gene-wise dispersions were estimated with estimateDisp(), and quasi-likelihood negative binomial generalized linear models were fitted with glmQLFit() [88]Differential expression testing was performed using glmQLFTest [88] and p-values were adjusted for multiple testing using the Benjamini–Hochberg false discovery rate (FDR) procedure. Functional gene enrichment was conducted using clusterProfiler v4.4.4 [89] in R v4.2.1. Significant genes as defined by FDR < 0.05 and were selected for overrepresentation analysis (ORA) using enrichGO [89]. ORA was performed separately for positively and negatively differentially expressed genes. The set of genes considered for differential expression testing was used as the background gene list.
Statistical analyses
Statistical analyses of the results were performed using GraphPad Prism v9.0, while analyses of RNA-seq results were done using R v4.2.1. Graphical visualization was performed using various R packages. “ComplexHeatmap” was used to visualize functional pathway analyses with modifications for cell annotation of p-values [90]. For Venn diagram creation the R package “ggVennDiagram” was used [91].
Supporting information
S1 Fig. Half maximal effective concentration (EC50) for L. mexicana promastigotes treated with trivalent antimony.
Effective concentration is the concentration (or dose) effective in producing a certain percentage of the maximal response and can be used to estimate the drug tolerance, (A) The colorimetric MTT assay was used to estimate the EC50 value. Three conditions were evaluated per experiment: parasites growing without SbIII (control, C), parasites growing under SbIII pressure (treatment, T), and medium without parasites (blank). The change from yellow (MTT) to purple (Formazan or reduced MTT) indicates cell viability. (B) The EC50 value was estimated by using a non-linear regression model (Sigmoidal model with four parameters). The X-axis is the log transform drug concentration. Y-axis represents the percentage of survival parasites and the arbitrary units of formazan obtained for the untreated parasites corresponded to 100%. Then the EC80 value was estimated based on the EC50 and Hill slope value by using the online calculator: https://www.graphpad.com/quickcalcs/ECanything2/. Trivalent antimony was administrated as potassium antimonyl tartrate (PAT). Three technical replicates and three biological replicates were performed (BR1–3). Mean and standard error are plotted. Degrees of Freedom: 13. R squared: 0.9711. Sum of Squares:244.3. Sy.x: 4.271. EC50: 0.1638 mM. Hill slope: -2.114. EC80: 0.315 mM. Lower (-). Higher (+).
https://doi.org/10.1371/journal.ppat.1014682.s001
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S2 Fig. Persister-like parasites were identified after the drug challenge.
Parental and persister-like parasites were observed by using an inverted light microscope after the recovery stage. (A) Parental or control group not treated with the drug showing normal parasite morphology (green arrows). (B) Drug treated group showing two different morphologies: apoptotic and live parasites. Multiple apoptotic parasites (red arrow). A percentage of survived persister-like parasites with normal morphology (green arrow). Trivalent antimony was administrated as potassium antimonyl tartrate (PAT) at the EC80 value.
https://doi.org/10.1371/journal.ppat.1014682.s002
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S3 Fig. Half maximal effective concentration (EC50) for L. mexicana promastigotes treated with trivalent antimony after recovery with ficoll.
Effective concentration is the concentration (or dose) effective in producing a certain percentage of the maximal response and can be used to estimate the drug tolerance. The colorimetric MTT assay was used to estimate the EC50 value. Three conditions were evaluated per experiment: parasites growing without SbIII (control, C), parasites growing under SbIII pressure (treatment, T), and medium without parasites (blank). The change from yellow (MTT) to purple (Formazan or reduced MTT) indicates cell viability. The EC50 value was estimated in Source_Data 1 by using a non-linear regression model (Sigmoidal model with four parameters). Trivalent antimony was administered as potassium antimonyl tartrate (PAT). Three technical replicates and three biological replicates were performed (BR1–3).
https://doi.org/10.1371/journal.ppat.1014682.s003
(PDF)
S4 Fig. Bioinformatic workflow for RNA-seq analysis of Leishmania mexicana.
Numbered boxes indicate workflow steps in logical order. Green boxes denote external reference files obtained from TriTrypDB and used during analysis. Gray box indicates raw input sequencing data. Tool names are shown in the box headers, the purpose of each step is described within the boxes, and the corresponding input (In) and output (Out) file types are indicated in the white panels. Yellow boxes denote read quality control assessment.
https://doi.org/10.1371/journal.ppat.1014682.s004
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S1 Data. Source data for Fig 2. Raw values for growth curves and MTT assays.
https://doi.org/10.1371/journal.ppat.1014682.s005
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S2 Data. Source data for Fig 4 and 6.
Differential gene expression of comparisons 1–6 (C1-6).
https://doi.org/10.1371/journal.ppat.1014682.s006
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S3 Data. Source data for Fig 5. Differential gene expression of ribosomal proteins for comparisons 1–6 (C1-6).
https://doi.org/10.1371/journal.ppat.1014682.s007
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S4 Data. Source data for Fig 7. List of genes for Venn diagram.
https://doi.org/10.1371/journal.ppat.1014682.s008
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Acknowledgments
We thank members of the Karamysheva and Phillips laboratories for their advice and critical discussions. Deep RNA-seq was performed by Admera Health. Figs 1 and 8 were created in BioRender.com.
References
- 1. Georgiadou SP, Makaritsis KP, Dalekos GN. Leishmaniasis revisited: Current aspects on epidemiology, diagnosis and treatment. J Transl Int Med. 2015;3(2):43–50. pmid:27847886
- 2. Akhoundi M, Downing T, Votýpka J, Kuhls K, Lukeš J, Cannet A, et al. Leishmania infections: Molecular targets and diagnosis. Mol Aspects Med. 2017;57:1–29. pmid:28159546
- 3. Cosma C, Maia C, Khan N, Infantino M, Del Riccio M. Leishmaniasis in humans and animals: A one health approach for surveillance, prevention and control in a changing world. Trop Med Infect Dis. 2024;9(11):258. pmid:39591264
- 4. McIlwee BE, Weis SE, Hosler GA. Incidence of endemic human cutaneous Leishmaniasis in the United States. JAMA Dermatol. 2018;154(9):1032–9. pmid:30046836
- 5. Hotez PJ. The rise of neglected tropical diseases in the “new Texas”. PLoS Negl Trop Dis. 2018;12(1):e0005581. pmid:29346369
- 6. Nepal B, McCormick-Baw C, Patel K, Firmani S, Wetzel DM. Cutaneous Leishmania mexicana infections in the United States: Defining strains through endemic human pediatric cases in northern Texas. mSphere. 2024;9(3):e0081423. pmid:38421172
- 7. Van den Bergh B, Fauvart M, Michiels J. Formation, physiology, ecology, evolution and clinical importance of bacterial persisters. FEMS Microbiol Rev. 2017;41(3):219–51. pmid:28333307
- 8. Helaine S, Kugelberg E. Bacterial persisters: Formation, eradication, and experimental systems. Trends Microbiol. 2014;22(7):417–24. pmid:24768561
- 9. Wuyts J, Van Dijck P, Holtappels M. Fungal persister cells: The basis for recalcitrant infections?. PLoS Pathog. 2018;14(10):e1007301. pmid:30335865
- 10. Barrett MP, Kyle DE, Sibley LD, Radke JB, Tarleton RL. Protozoan persister-like cells and drug treatment failure. Nat Rev Microbiol. 2019;17(10):607–20. pmid:31444481
- 11. Decollogny M, Rottenberg S. Persisting cancer cells are different from bacterial persisters. Trends Cancer. 2024;10(5):393–406. pmid:38429144
- 12. Niu H, Gu J, Zhang Y. Bacterial persisters: Molecular mechanisms and therapeutic development. Signal Transduct Target Ther. 2024;9(1):174. pmid:39013893
- 13. Oren Y, Tsabar M, Cuoco MS, Amir-Zilberstein L, Cabanos HF, Hütter J-C, et al. Cycling cancer persister cells arise from lineages with distinct programs. Nature. 2021;596(7873):576–82. pmid:34381210
- 14. Fisher RA, Gollan B, Helaine S. Persistent bacterial infections and persister cells. Nat Rev Microbiol. 2017;15(8):453–64. pmid:28529326
- 15. Keren I, Shah D, Spoering A, Kaldalu N, Lewis K. Specialized persister cells and the mechanism of multidrug tolerance in Escherichia coli. J Bacteriol. 2004;186(24):8172–80. pmid:15576765
- 16. Keren I, Minami S, Rubin E, Lewis K. Characterization and transcriptome analysis of Mycobacterium tuberculosis persisters. mBio. 2011;2(3):e00100-11. pmid:21673191
- 17. Oren Y. Standing on the shoulders of microbes: How cancer biologists are expanding their view of hard-to-kill persister cells. Mol Syst Biol. 2022;18(7):e11168. pmid:35866381
- 18. Shen S, Faouzi S, Bastide A, Martineau S, Malka-Mahieu H, Fu Y, et al. An epitranscriptomic mechanism underlies selective mRNA translation remodelling in melanoma persister cells. Nat Commun. 2019;10(1):5713. pmid:31844050
- 19. Windels EM, Ben Meriem Z, Zahir T, Verstrepen KJ, Hersen P, Van den Bergh B, et al. Enrichment of persisters enabled by a ß-lactam-induced filamentation method reveals their stochastic single-cell awakening. Commun Biol. 2019;2:426. pmid:31815194
- 20. de Oliveira BCD, Arias Del Angel J, Herrmann May N, Barrett MP, Domagalska MA, Beneke T. Beyond resistance: Emerging methods to dissect drug responses in Leishmania. PLoS Pathog. 2026;22(6):e1014331. pmid:42340977
- 21. Jara M, Arevalo J, Llanos-Cuentas A, den Broeck FV, Domagalska MA, Dujardin J-C. Unveiling drug-tolerant and persister-like cells in Leishmania braziliensis lines derived from patients with cutaneous leishmaniasis. Front Cell Infect Microbiol. 2023;13:1253033. pmid:37790908
- 22. Pan X, Liu W, Du Q, Zhang H, Han D. Recent advances in bacterial persistence mechanisms. Int J Mol Sci. 2023;24(18):14311. pmid:37762613
- 23. Miyaue S, Suzuki E, Komiyama Y, Kondo Y, Morikawa M, Maeda S. Bacterial memory of persisters: bacterial persister cells can retain their phenotype for days or weeks after withdrawal from colony-biofilm culture. Front Microbiol. 2018;9:1396. pmid:29997606
- 24. Lissek T. Learning and memory in molecular networks. Biochem Biophys Res Commun. 2025;788:152805. pmid:41124798
- 25. Riber L, Hansen LH. Epigenetic memories: the hidden drivers of bacterial persistence?. Trends Microbiol. 2021;29(3):190–4.
- 26. Pashay Ahi E. Epitranscriptomics as a candidate universal modulator of dormancy transitions. Ecol Evol. 2026;16(2):e73007. pmid:41646876
- 27. Zou J, Peng B, Qu J, Zheng J. Are bacterial persisters dormant cells only?. Front Microbiol. 2021;12:708580.
- 28. Vázquez-Laslop N, Lee H, Neyfakh AA. Increased persistence in Escherichia coli caused by controlled expression of toxins or other unrelated proteins. J Bacteriol. 2006;188(10):3494–7. pmid:16672603
- 29. Kwan BW, Valenta JA, Benedik MJ, Wood TK. Arrested protein synthesis increases persister-like cell formation. Antimicrob Agents Chemother. 2013;57(3):1468–73. pmid:23295927
- 30. Blattman SB, Jiang W, McGarrigle ER, Liu M, Oikonomou P, Tavazoie S. Identification and genetic dissection of convergent persister cell states. Nature. 2024;636(8042):438–46. pmid:39506104
- 31. Guo L, Lee Y-T, Zhou Y, Huang Y. Targeting epigenetic regulatory machinery to overcome cancer therapy resistance. Semin Cancer Biol. 2022;83:487–502. pmid:33421619
- 32. Russo M, Chen M, Mariella E, Peng H, Rehman SK, Sancho E, et al. Cancer drug-tolerant persister cells: From biological questions to clinical opportunities. Nat Rev Cancer. 2024;24(10):694–717. pmid:39223250
- 33. Kiboi D, Sá JM, Nayak A, Micchelli CE, Amin SN, Burbelo AG, et al. Isolation and characterization of Plasmodium falciparum blood-stage persisters by improved selection protocols using dihydroartemisinin alone. Antimicrob Agents Chemother. 2025;69(3):e0005324. pmid:39927767
- 34. Tripathi J, Stoklasa M, Nayak S, En Low K, Qian Hui Lee E, Duong Tien QH, et al. The artemisinin-induced dormant stages of Plasmodium falciparum exhibit hallmarks of cellular quiescence/senescence and drug resilience. Nat Commun. 2024;15(1):7485. pmid:39209862
- 35. Sánchez-Valdéz FJ, Padilla A, Wang W, Orr D, Tarleton RL. Spontaneous dormancy protects Trypanosoma cruzi during extended drug exposure. Elife. 2018;7:e34039. pmid:29578409
- 36. Jayawardhana S, Ward AI, Francisco AF, Lewis MD, Taylor MC, Kelly JM, et al. Benznidazole treatment leads to DNA damage in Trypanosoma cruzi and the persistence of rare widely dispersed non-replicative amastigotes in mice. PLoS Pathog. 2023;19(11):e1011627. pmid:37956215
- 37. Tarannum A, Rodríguez-Almonacid CC, Salazar-Bravo J, Karamysheva ZN. Molecular mechanisms of persistence in protozoan parasites. Microorganisms. 2023;11(9):2248. pmid:37764092
- 38. Mandell MA, Beverley SM. Continual renewal and replication of persistent Leishmania major parasites in concomitantly immune hosts. Proc Natl Acad Sci U S A. 2017;114(5):E801–10. pmid:28096392
- 39. Moncada-Diaz MJ, Rodríguez-Almonacid CC, Quiceno-Giraldo E, Khuong FTH, Muskus C, Karamysheva ZN. Molecular mechanisms of drug resistance in Leishmania spp. Pathogens. 2024;13(10):835. pmid:39452707
- 40. Bhusal CK, Sinha S, Kaur D, Sehgal R. Unravelling drug resistance in leishmaniasis: Genomic adaptations and emerging therapies. Front Mol Biosci. 2025;12:1573618. pmid:40492115
- 41. Gutierrez Guarnizo SA, Karamysheva ZN, Galeano E, Muskus CE. Metabolite biomarkers of leishmania antimony resistance. Cells. 2021;10(5).
- 42. Gutierrez Guarnizo SA, Tikhonova EB, Zabet-Moghaddam M, Zhang K, Muskus C, Karamyshev AL, et al. Drug-induced lipid remodeling in leishmania parasites. Microorganisms. 2021;9(4):790.
- 43. Gutierrez Guarnizo SA, Tikhonova EB, Karamyshev AL, Muskus CE, Karamysheva ZN. Translational reprogramming as a driver of antimony-drug resistance in Leishmania. Nat Commun. 2023;14(1):2605. pmid:37147291
- 44. Jara M, Barrett M, Maes I, Regnault C, Imamura H, Domagalska MA, et al. Transcriptional shift and metabolic adaptations during Leishmania quiescence using stationary phase and drug pressure as models. Microorganisms. 2022;10(1):97. pmid:35056546
- 45. Hernández-Elvira M, Sunnerhagen P. Post-transcriptional regulation during stress. FEMS Yeast Res. 2022;22(1):foac025. pmid:35561747
- 46. Grünebast J, Lorenzen S, Clos J. Genome-wide quantification of polycistronic transcription in Leishmania major. mBio. 2025;16(1):e0224124. pmid:39584812
- 47. Bersaglieri C, Santoro R. Genome organization in and around the nucleolus. Cells. 2019;8(6):579. pmid:31212844
- 48. Grünebast J, Clos J. Leishmania: Responding to environmental signals and challenges without regulated transcription. Comput Struct Biotechnol J. 2020;18:4016–23. pmid:33363698
- 49. Karamysheva ZN, Gutierrez Guarnizo SA, Karamyshev AL. Regulation of translation in the protozoan parasite Leishmania. Int J Mol Sci. 2020;21(8):2981. pmid:32340274
- 50. Advani VM, Ivanov P. Translational control under stress: reshaping the translatome. Bioessays. 2019;41(5):e1900009.
- 51. Genuth NR, Dillin A. Translational regulation in stress biology. Nat Cell Biol. 2025;27(10):1609–21. pmid:41057652
- 52. Patino LH, Muskus C, Ramírez JD. Transcriptional responses of Leishmania (Leishmania) amazonensis in the presence of trivalent sodium stibogluconate. Parasit Vectors. 2019;12(1):348. pmid:31300064
- 53. Garg M, Goyal N. MAPK1 of Leishmania donovani modulates antimony susceptibility by downregulating P-glycoprotein efflux pumps. Antimicrob Agents Chemother. 2015;59(7):3853–63. pmid:25870075
- 54. Légaré D, Hettema E, Ouellette M. The P-glycoprotein-related gene family in Leishmania. Mol Biochem Parasitol. 1994;68(1):81–91. pmid:7891750
- 55. Freitas-Mesquita AL, Dos-Santos ALA, Meyer-Fernandes JR. Involvement of Leishmania phosphatases in parasite biology and pathogeny. Front Cell Infect Microbiol. 2021;11:633146. pmid:33968798
- 56. Madusanka RK, Karunaweera ND, Silva H, Selvapandiyan A. Antimony resistance and gene expression in Leishmania: Spotlight on molecular and proteomic aspects. Parasitology. 2024;151(1):1–14. pmid:38012864
- 57. Andrade JM, Gonçalves LO, Liarte DB, Lima DA, Guimarães FG, de Melo Resende D, et al. Comparative transcriptomic analysis of antimony resistant and susceptible Leishmania infantum lines. Parasit Vectors. 2020;13(1):600. pmid:33256787
- 58. Mukherjee S, Sen Santara S, Das S, Bose M, Roy J, Adak S. NAD(P)H cytochrome b5 oxidoreductase deficiency in Leishmania major results in impaired linoleate synthesis followed by increased oxidative stress and cell death. J Biol Chem. 2012;287(42):34992–5003. pmid:22923617
- 59. Zhao X, Guan S. Epitranscriptomic control of cancer immunity and therapy resistance. Front Immunol. 2025;16:1706557. pmid:41280938
- 60. Yuan F, Zhang W, Xia Y, Zhou X, Gao H. The role and mechanism of METTL3 in cancer: Emerging insights into m6A methylation and therapeutic potential. Eur J Med Res. 2025;30(1):1017. pmid:41139780
- 61. Farooq Z, Banday S, Pandita TK, Altaf M. The many faces of histone H3K79 methylation. Mutat Res Rev Mutat Res. 2016;768:46–52. pmid:27234562
- 62. Dejung M, Subota I, Bucerius F, Dindar G, Freiwald A, Engstler M, et al. Quantitative proteomics uncovers novel factors involved in developmental differentiation of Trypanosoma brucei. PLoS Pathog. 2016;12(2):e1005439. pmid:26910529
- 63. de Almeida RF, Fernandes M, Figueiredo FB, de Godoy LMF. Histone post-translational modifications of Leishmania braziliensis. Sci Data. 2025;12(1):1899. pmid:41339363
- 64. Ferreira TR, Dowle AA, Parry E, Alves-Ferreira EVC, Hogg K, Kolokousi F, et al. PRMT7 regulates RNA-binding capacity and protein stability in Leishmania parasites. Nucleic Acids Res. 2020;48(10):5511–26. pmid:32365184
- 65. Alcoforado Diniz J, Chaves MM, Vaselek S, Miserani Magalhães RD, Ricci-Azevedo R, de Carvalho RVH, et al. Protein methyltransferase 7 deficiency in Leishmania major increases neutrophil associated pathology in murine model. PLoS Negl Trop Dis. 2021;15(3):e0009230. pmid:33651805
- 66. Uguen M, Liu T, James LI, Frye SV. Tudor-Containing Methyl-Lysine and Methyl-Arginine reader proteins: Disease implications and chemical tool development. ACS Chem Biol. 2025;20(1):33–47. pmid:39718819
- 67. Achour C, Oberdoerffer S. NAT10 and cytidine acetylation in mRNA: intersecting paths in development and disease. Curr Opin Genet Dev. 2024;87:102207. pmid:38820741
- 68. Bezerra AM, Leite AB, de Souza Reis CR, de Lemos Padilha Pitta JL, Maran SR, Moretti NS, et al. Impact on the Leishmania mexicana transcriptome due to knockout of genes encoding orthologs of methyltransferases involved in m1A and m5C mRNA modifications. Parasit Vectors. 2025;18(1):315. pmid:40745619
- 69. Priya B, Nawaz A, Rai AK, Ali V. Role of dithiol glutaredoxin 1 protein of Leishmania donovani in Amphotericin B resistance and redox homeostasis. Microb Pathog. 2025;205:107659. pmid:40348208
- 70. Williams RAM, Kelly SM, Mottram JC, Coombs GH. 3-Mercaptopyruvate sulfurtransferase of Leishmania contains an unusual C-terminal extension and is involved in thioredoxin and antioxidant metabolism. J Biol Chem. 2003;278(3):1480–6. pmid:12419809
- 71. Sharma SV, Lee DY, Li B, Quinlan MP, Takahashi F, Maheswaran S, et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell. 2010;141(1):69–80. pmid:20371346
- 72. El Meouche I, Jain P, Jolly MK, Capp J-P. Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity. Transl Oncol. 2024;49:102069. pmid:39121829
- 73. Audas TE, Jacob MD, Lee S. The nucleolar detention pathway: A cellular strategy for regulating molecular networks. Cell Cycle. 2012;11(11):2059–62. pmid:22580471
- 74. González-Arzola K. The nucleolus: Coordinating stress response and genomic stability. Biochim Biophys Acta Gene Regul Mech. 2024;1867(2):195029. pmid:38642633
- 75. McConville MJ. Metabolic crosstalk between Leishmania and the macrophage host. Trends Parasitol. 2016;32(9):666–8. pmid:27234812
- 76. Saunders EC, Sernee MF, Ralton JE, McConville MJ. Metabolic stringent response in intracellular stages of Leishmania. Curr Opin Microbiol. 2021;63:126–32. pmid:34340099
- 77. Mandell MA, Beatty WL, Beverley SM. Quantitative single-cell analysis of Leishmania major amastigote differentiation demonstrates variably extended expression of the lipophosphoglycan (LPG) virulence factor in different host cell types. PLoS Negl Trop Dis. 2022;16(10):e0010893. pmid:36302046
- 78. McConville MJ, Saunders EC, Kloehn J, Dagley MJ. Leishmania carbon metabolism in the macrophage phagolysosome- feast or famine?. F1000Res. 2015;4(F1000 Faculty Rev):938. pmid:26594352
- 79. Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355(6327):826–30. pmid:28183996
- 80. Alvarez-Jarreta J, Amos B, Aurrecoechea C, Bah S, Barba M, Barreto A, et al. VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023. Nucleic Acids Res. 2024;52(D1):D808–16. pmid:37953350
- 81.
Dainat J, Cannoodt R, Soares A, Ruano DG, Hereñú D, Murray DKD. NBISweden/AGAT: AGAT v1.5.1 (v1.5.1). 2025.
- 82. Chen S. fastp 1.0: An ultra-fast all-round tool for FASTQ data quality control and preprocessing. Imeta. 2025;4(5):e70078. pmid:41112039
- 83.
Andrews S. FastQC: A quality control tool for high throughput sequence data. 2010. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/
- 84. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. pmid:27312411
- 85. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. pmid:23104886
- 86. Liao Y, Smyth GK, Shi W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–30. pmid:24227677
- 87.
Carlson M, Pagès H. AnnotationForge: Tools for building SQLite-based annotation data packages. 2025. https://bioconductor.org/packages/AnnotationForge
- 88. Chen Y, Chen L, Lun ATL, Baldoni PL, Smyth GK. edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Res. 2025;53(2):gkaf018. pmid:39844453
- 89. Xu S, Hu E, Cai Y, Xie Z, Luo X, Zhan L, et al. Using clusterProfiler to characterize multiomics data. Nat Protoc. 2024;19(11):3292–320. pmid:39019974
- 90. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32(18):2847–9. pmid:27207943
- 91. Gao CH, Yu G, Cai P. ggVennDiagram: An intuitive, easy-to-use, and highly customizable R package to generate Venn diagram. Front Genet. 2021;12:706907.