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. Author manuscript; available in PMC: 2024 Sep 1.
Published in final edited form as: J Immunol. 2024 Mar 1;212(5):894–903. doi: 10.4049/jimmunol.2300576

Consolidation of a molecular signature of healing in cutaneous leishmaniasis is achieved during the first ten days of treatment

Lina Giraldo-Parra *,†, David E Rebellón-Sánchez *,‡, Adriana Navas *,§, Ashton Trey Belew ¶,||, Najib M El-Sayed ¶,||, María Adelaida Gómez *,†,#
PMCID: PMC10922383  NIHMSID: NIHMS1954409  PMID: 38231122

Abstract

The immune response is central to the pathogenesis of cutaneous leishmaniasis (CL). However, most of our current understanding of the immune response in human CL derives from the analysis of systemic responses, which only partially reflect what occurs in the skin. Here, we characterized the transcriptional dynamics of skin lesions during the course of treatment of CL patients and identified gene signatures and pathways associated with healing and non-healing responses. We performed a comparative transcriptome profiling of serial skin lesion biopsies obtained before, in the middle, and at the end of treatment of CL patients (8 who cured and 8 with treatment failure). Lesion transcriptomes from patients who healed revealed recovery of the stratum corneum, suppression of T cell-mediated inflammatory response and damping of neutrophil activation, as early as 10 days after initiation of treatment. These transcriptional programs of healing were consolidated before lesion re-epithelization. In stark contrast, down-regulation of genes involved in keratinization was observed throughout treatment in patients who did not heal, indicating that in addition to uncontrolled inflammation, treatment failure during CL is mediated by impaired mechanisms of wound healing. This work provides insights into the factors that contribute to the effective resolution of skin lesions caused by L. Viannia, sheds light on the consolidation of transcriptional programs of healing and non-healing responses before the clinically apparent resolution of skin lesions, and identifies inflammatory and wound healing targets for host-directed therapies for CL.

Keywords: cutaneous leishmaniasis, Leishmania Viannia, skin biopsies, RNA-seq, transcriptional dynamics

INTRODUCTION

The clinical outcome of cutaneous leishmaniasis (CL), and the mechanisms involved in lesion development and healing, rely on the infecting parasite species as well as on the host immune response (1). In patients infected with Leishmania (Viannia) species, inflammation participates in healing and immunological protection, as well as in the pathology (2–5). For example, CD4+ T cells are critical for controlling parasite growth by producing IFNγ and TNFα, which activate macrophages for intracellular parasite killing (2, 4). Conversely, these same cytokines contribute to immunopathogenesis and disease chronicity via a strong pro-inflammatory and deregulated immune response (2, 6). Despite this knowledge, immunological readouts are rarely evaluated in antileishmanial drug development pipelines; and when considered, they are ascribed to the dichotomy of systemic Th1 and Th2 responses being protective and pathological, respectively (7).

The relationship between systemic immune profiles and skin-specific immune response in CL patients is only partially understood. Illustrating this, a transcriptomic study of lesion biopsies collected before initiation of antileishmanial treatment from patients infected with L. (V.) braziliensis, showed that those patients who do not cure overexpress genes related to CD8+ T cells and the cytolytic pathways (8, 9). However, CD8+ T cell functions had not been previously highlighted as contributors to therapeutic responses in studies of the systemic immune response. Similarly, a recent study by our group showed that peripheral blood mononuclear cells (PBMCs) from CL patients (either at rest or re-stimulated with Leishmania), only partially reflected the immunological changes observed in cutaneous lesions during exposure to antimonial drugs. The repertoire of genes modulated in PBMCs was one third of that from skin lesions, substantially underrepresenting gene modules associated with Th17 and polymorphonuclear cell responses (10).

The direct evaluation of lesion samples is important for the investigation of immunological determinants of healing and non-healing responses in CL. A more complete understanding of these processes translates into the identification of potential host targets for intervention. In this study, we sought to characterize the clinical progression and transcriptional dynamics of skin lesions during the treatment of CL patients, and to identify gene signatures and pathways associated with healing and non-healing responses. Understanding the responses associated with the outcome of treatment will inform the development of new host-directed therapies for CL, the optimization of available treatments, and the development of predictive and prognostic tool for assessment of outcomes.

MATERIALS AND METHODS

Ethics statement:

This study was carried out at the Centro Internacional de Entrenamiento e Investigaciones Médicas (CIDEIM) in the cities of Cali and Tumaco, Colombia. Study protocols, consent forms, and all procedures were approved by the CIDEIM Institutional Review Board (study code CIEIH-1258) for the ethical conduct of research involving human subjects, in compliance with national (resolution 008430, República de Colombia, Ministerio de Salud, 1993) and international (Declaration of Helsinki and amendments, World Medical Association, Fortaleza, Brazil, October 2013) guidelines. All participants provided written informed consent.

Study design, patients and samples:

We characterized the clinical progression and transcriptional dynamics of skin lesions during the treatment of 16 adult patients with parasitological diagnosis of CL, time of disease evolution ≤ 6 months (time from onset of the lesion to the time the patient sought medical care), and absence of mucosal compromise (Schematic representation in Figure S1). Patients were treated with Glucantime (GLUC) or Miltefosine (MLF), following standard-of-care guidelines. Clinical outcome was evaluated at week 13 after initiation of treatment (day 90 ± 7) and week 26 (day 180 ± 7). Cure was defined as complete epithelialization of all lesions with no signs of reactivation by follow-up at week 13 for GLUC, or week 26 for MLF. Patients with therapeutic failure (TF) were defined as those with lesions presenting local inflammatory reactions or lack of complete re-epithelization in any of the lesions, relapse (lesion enlargement) after apparent cure, or the appearance of a new CL lesion.

Three lesion biopsies (3mm) were obtained from each patient: the first before initiation of treatment (Pre-Tx), a second mid-way through treatment (Mid-Tx: day 10 ± 3 for GLUC participants or day 14 ± 3 for MLF), and a final one at the end of treatment (EoTx: day 20 for GLUC, and day 28 for MLF). The punch biopsy encompassed 1/3 of healthy skin and 2/3 of the edge of the lesion (indurated edge, which does not include necrotic tissue). Biopsies were processed for RNA extraction, parasite load assessment and preliminary gene expression analysis of 27 inflammatory genes by RT-qPCR (10). Samples from 12 of the 16 participants were used for whole transcriptome evaluation by RNA-seq.

Tissue processing and RNA extraction:

Skin biopsies were immediately stored in 1 mL Allprotect® (QIAGEN, Cat.76405), equilibrated overnight at 4°C, and stored at −20°C until processing. Tissue samples were processed as previously described (11). Briefly, samples were cut into smaller fractions and homogenized using a manual glass tissue grinder on an ice bath containing TRIzol™ (Invitrogen, Cat. 15596026). The lysate was centrifuged, RNA extracted using chloroform and cleaned with RNA purification columns (RNeasy Mini Kit, QIAGEN, Cat. 74104). RNA was eluted from columns in 25 μL of RNase-free water (QIAGEN, Cat. 129112) and stored at −80°C until use. The quantity and purity of the extracted RNA was evaluated using a Nanodrop ND-1000 spectrophotometer. The RNA integrity was evaluated using an Agilent 2100 Bioanalyzer (RNA 6000 Nano LabChip, Agilent Technologies, Cat. 5067–1511); an RNA integrity number (RIN) value ≥7 was determined acceptable.

cDNA synthesis and RT-qPCR:

cDNA was synthesized using a RT first-strand synthesis kit (QIAGEN, Cat. 330404) according to the manufactureŕs instructions. The parasite burden was measured by RT-qPCR for detection of Leishmania 7SLRNA as previously described (12). The human peptidylprolyl isomerase B (PPIB) gene was amplified and used to normalize the parasite load to the number of human cells in the sample. qPCR reactions were performed with SYBR Green Master Mix (Applied Biosystems, cat. 4364346) on a CFX96 Real-Time System thermocycler (Bio-Rad). For absolute quantification, standard curves were produced from serial dilution of cDNA products obtained from 1×107 L. (V.) panamensis promastigotes and 1×107 cells from the human cell line U937. An initial exploration of the expression profile of 27 inflammatory genes in CL lesion biopsies was done using custom-made RT-qPCR arrays (RT2 Profiler™ PCR Array technology, QIAGEN, ref. No. CLAH23658D). The inflammatory mediators were selected based on previous gene expression profiling data from CL biopsies (10). Gene expression was normalized to GAPDH and RPLP0. Data was analyzed using the ΔΔCt method and fold change (FC) calculated compared to Pre-Tx biopsies and expressed as 2−ΔΔCt. Data was processed and analyzed on the RT2 Profiler™ PCR Array Data Analysis online tool provided by the manufacturer.

RNA-Seq:

Construction of cDNA libraries was performed using the Illumina TruSeq® Stranded mRNA kit V2 (Cat. 20020594). cDNA library quality controls were carried out using the Agilent 2100 Bioanalyzer (DNA 1000 kit, Agilent Technologies, Cat. 5067–1505). Paired end reads were obtained using an Illumina HiSeq1000 at the Brain & Behavior Institute - Advanced Genomic Technologies Core (BBI-AGTC) at the University of Maryland, College Park, MD. Trimmomatic (13) was used to remove Illumina adapter sequences, discard reads shorter than 40 nucleotides, and trim any 4 nucleotide rolling window with a mean Phred quality score less than or equal to 20. Sequence quality metrics were assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Reads were aligned against the human (hg38 revision 100), L.(V.) panamensis (TriTrypDb release 36), and L. (V.) braziliensis (release 26) genomes with HISAT2 (2.1.0) (14) using the default parameters. The mapped reads were sorted and indexed via samtools (15) and passed to HTSeq (16) for generating count tables. In addition, transcript abundances were simultaneously quantified via salmon (1.4.0) (17).

RNA-seq data analysis and interpretation.

Statistical analyses for RNA-seq differential expression (DE) included pairwise comparisons using the Bioconductor packages: limma (18), edgeR (19), DESeq2 (20), EBSeq (21), and a statistically uninformed basic analysis. The DE methods (except basic and EBSeq) were performed using an experimental model comprised of the factor of interest for each specific DE contrast (i.e. (visit number, infecting species, drug, and clinical outcome), followed by surrogate variable estimates provided by SVA (22).The surrogate variable estimates were calculated using the unsupervised ‘svaseq’ function using the filtered data, conditional model, and null model. The number of random effect variables was estimated as described in (23). These estimates were created via the function all_adjusters located in normalize_batch.R in the hpgltools package and appended to the model used by limma, DESeq2, and edgeR, (https://github.com/elsayed-lab/CL_biopsies_Colombia). The quality of each contrast was evaluated by the degree of agreement among methods, but the interpretations were primarily informed by the DESeq2 results. Genes with significant changes in abundance (Log2FC ≥ |1| and false discovery rate (FDR) adjusted P values ≤ 0.05) and that were uniquely differentially expressed in either cures or TF (in the contrast of Mid-Tx vs. Pre-Tx and EoTx vs. Pre-Tx lesion transcriptomes), were used as input for gene enrichment analysis using Reactome v.85, 2023 (24). Interaction networks were constructed using String v. 11.5.

Statistical analyses:

Univariate analyses were performed to explore and describe the sociodemographic and clinical variables. Categorical variables were described with frequencies and percentages. Quantitative variables were described as means (±SD) or medians (IQR) according to the distribution of the data. For the comparison of qualitative variables, Fisher’s exact test or the chi2 test was used according to the data. Quantitative variables were compared using t-test or U-Mann-Whitney test according to the normal distribution of the data. Normality was determined with qq plots and the Shapiro wilk test. RT-qPCR gene expression data (2−Δct) were log-transformed and used as input for network analysis using Graphia Professional Software (Kajeka Ltd, UK) (25) with a parameter set to the Spearman |ρ|> 0.75. The Markov clustering algorithm (26) embedded within Graphia was used for unsupervised clustering of gene expression datasets. Statistical significance was estimated by the Mann-Whitney test or t-test, depending on the distribution of the data. In all analyses P values ≤ 0.05 were considered significant. Statistical analysis was performed using GraphPad Prism version 9 and R version 4.1.2.

RESULTS

Clinical and sociodemographic characteristics of study participants

Clinical and demographic data was obtained for 16 participants, eight who cured and eight with TF. None of the participants had previous history of CL. Participants in both groups were predominantly young Afro-Colombian men. Ten patients were administered treatment with GLUC, and 6 with MLF. Although no statistically significant differences were found for sociodemographic characteristics between groups (Table I), patients with TF had less time of lesion evolution at the time of initiation of treatment (1 month, IQR 1.0 – 1.6) compared to those who cured (2 months, IQR: 1.4 – 2). The proportion of patients with L. (V.) braziliensis infection, and patients treated with MLF was higher in TF than in cures (50% vs. 12.5% and 50% vs. 25%, respectively), but neither difference was statistically significant. Adherence to treatment was ≥ 95% among all participants (Table I). There were no differences in the number and severity of adverse drug reactions reported per patient. No patients had serious adverse events.

TABLE I.

Clinical and socio-demographic characteristics of study participants

Characteristic Overall, n = 161 Cure, n = 81 TF, n = 81 P-value2
Sex >0.9
 Male 14.0 (87.5%) 7.0 (87.5%) 7.0 (87.5%)
 Female 2.0 (12.5%) 1.0 (12.5%) 1.0 (12.5%)
Ethnicity >0.9
 Afrocolombian 10.0 (62.5%) 5.0 (62.5%) 5.0 (62.5%)
 Indigenous 3.0 (18.8%) 2.0 (25.0%) 1.0 (12.5%)
 Mestizo 3.0 (18.8%) 1.0 (12.5%) 2.0 (25.0%)
Age, years 27 (23 – 33) 27 (25 – 31) 26 (23 – 36) >0.9
Body Mass Index 23.7 (22.4 – 26.7) 23.5 (23.0 – 26.7) 23.9 (22.3 – 27.1) >0.9
Time of evolution, months 1.5 (1.0 – 2.0) 2.0 (1.4 – 2.0) 1.0 (1.0 – 1.6) 0.11
Number of lesions 2.0 (1.0 – 2.2) 2.0 (1.0 – 2.2) 1.5 (1.0 – 2.2) 0.7
Type of skin lesion
 Ulcer 16.0 (100.0%) 8.0 (100.0%) 8.0 (100.0%)
Evolution of the lesion at the end of treatment 0.1
 Ulcer to Plaque 9.0 (56.2%) 6.0 (75.0%) 3.0 (37.5%)
 Ulcer to Scar 3.0 (18.8%) 2.0 (25.0%) 1.0 (12.5%)
 Ulcer to Ulcer 4.0 (25.0%) 4.0 (50.0%)
Regional lymphadenopathy 3.0 (18.8%) 1.0 (12.5%) 2.0 (25.0%) >0.9
Specie 0.3
 L. (V.) panamensis 11.0 (68.8%) 7.0 (87.5%) 4.0 (50.0%)
 L. (V.) braziliensis 5.0 (31.2%) 1.0 (12.5%) 4.0 (50.0%)
Prescribed medicine 0.6
 Glucantime 10.0 (62.5%) 6.0 (75.0%) 4.0 (50.0%)
 Miltefosine 6.0 (37.5%) 2.0 (25.0%) 4.0 (50.0%)
Patients with adverse events (includes adverse drug reaction) 12 (75%) 7 (88%) 5 (62%) 0.6
Number of adverse events 4.00 (4.00, 5.00) 4.00 (4.00, 5.00) 4.50 (4.00, 5.00) 0.8
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1

n (%); Median (IQR).

2

Fisher’s exact test; Wilcoxon rank sum exact test; Wilcoxon rank sum test.

TF: therapeutic failure.

Clinical and parasitological characteristics of CL lesions and their evolution during the course of treatment

All participants had ulcerated lesions, with a median of two lesions per patient (IQR 1.0–2.2) (Table I). The clinical evolution of the lesions was monitored during the course of treatment (Figure 1). Clinical evaluation by Mid-Tx showed that 81% of patients (13/16) had a persistent ulcer-like lesion, and only 19% (3/16) progressed to plaque. All ulcers that evolved to plaques healed. For 56% (9/16) of patients, ulcers progressed to plaques by EoTx, 25% persisted with an ulcer, and only 19% progressed to scar. It is noteworthy that in patients with TF, 50% had ulcers at EoTx, and only 12.5% progressed to scarring. In contrast, none of the patients who cured had ulcers by EoTx, 75% progressed to plaque and 25% had scarring (Table I). Although the ulcer size at the time of diagnosis was similar for all patients, the size became larger in MLF-treated patients at Mid-Tx and EoTx assessments, compared to those treated with GLUC (Figure 2A). Also, ulcer size was significantly larger in patients infected with L. (V.) braziliensis than in those infected with L. (V.) panamensis (Figure 2C). Despite larger ulcer size at EoTx in patients infected with L. (V.) braziliensis and in those treated with MLF, changes in the parasite load were not influenced by the type of treatment received (GLUC or MLF), nor by the infecting species, with parasitemias virtually undetectable by EoTx (Figure 2B, D).

Figure 1. Lesion progression during the course of treatment of CL patients.

Figure 1.

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Photographs of CL lesions from each study participant (n=16), from which biopsies were taken. Pre-treatment (Pre-Tx), middle of treatment (Mid-Tx), and end of treatment (EoTx). Parasite load in lesions at each time point for each patient are shown in the right panel.

Figure 2. Clinical and parasitological characteristics of CL lesions.

Figure 2.

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The lesion size of all patients, quantified as the area of the ulcer in mm2 (left panel), and the parasite burden quantified by RT-qPCR from lesion biopsy samples (right panel) are reported at three time points: pre-treatment (Pre-Tx), middle of treatment (Mid-Tx), and end of treatment (EoTx). Panels A and B show samples differenting patients who received treatment with Glucantime (GLUC, n=10) from those who received Miltefosine (MLF, n=6); by species (C and D) L. (V.) panamensis (L. (V.) p.) (n=11) and L. (V.) braziliensis (L. (V.) b.) (n=5); and by therapeutic outcome (E and F), Cure (n=8) and therapeutic failure (TF) (n=8). Data are presented as the mean ± SEM and statistical significance was estimated by the Mann-Whitney test. *: P < 0.05.

In the context of therapeutic responsiveness, ulcer size at Mid-Tx and EoTx was larger in patients with TF (P value = 0.03) (Figure 2E). Similarly, lesions from TF showed significantly higher Pre-Tx parasite load compared to those who healed. Although by Mid-Tx parasite loads were below 1 parasite for every 1000 human cells in both cures and TF, and almost unquantifiable at EoTx, loads were higher but not statistically significant in TF (Figure 2F).

Expression of inflammatory genes is similar in lesion biopsies of CL patients treated with GLUC or MLF.

The use of two different antileishmanial drugs in our study demanded evaluation of the influence of the drug in the in vivo modulation of gene expression. We therefore conducted an exploratory assessment of the dynamics of expression by RT-qPCR quantification of a set of 27 inflammatory genes previously shown to be modulated in CL lesions by GLUC treatment (10). The transcriptional effects elicited by exposure to GLUC or MLF were similar in terms of the directionality of the expression dynamics (Figure 3). This involved down-regulation, by EoTx, of pro-inflammatory gene transcripts (ccl2, ccl7, ccr2, cxcl3, il6, tnfα, cd14, il1α, il1β, cxcl9, cxcl10, ifnγ, cxcl2 and cxcl8/il8) (Figure 3), and up-regulation or no change in Th17-related genes (il17, il22, il23a, and il23r) (Figure 3). The expression dynamics of these genes was captured in four gene co-expression modules: Cluster A (cxcl9, cxcl10, ifnγ, il6, ccl7, ccl2, cxcl2 and cxcl3), cluster B (il1α and il8), Cluster C (il22 and il23a) and Cluster D (tnf and csf1) (Figure S2). Results from this exploratory and focused analysis, suggested that there are minimal differences in the gene expression profiles of CL lesion biopsies in patients treated with MLF or GLUC.

Figure 3. Gene expression profiles of lesion biopsies from CL patients during antileishmanial treatment.

Figure 3.

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Quantitative differences in gene expression during antileishmanial drug treatment in skin lesion biopsies from CL patients treated with GLUC (n=10) (black line) or MLF (n=6) (grey line), before (Pre-Tx), middle (Mid-Tx) and at the end of treatment (EoTx). Gene expression levels of 27 inflammatory genes (chemokines, cytokines and receptors) are represented as mean ± SEM of Log2 (2−ΔCt) values. Statistical significance was estimated by the Mann-Whitney test, contrasting both treatments at each time point. *: P < 0.05; **: P < 0.01.

Mechanisms of drug-induced wound healing stabilize as early as 10 days after initiation of treatment

To conduct an in-depth analysis of the transcriptomic changes occurring in lesions from patients who cure or who experience TF, lesion biopsies from 12 of the 16 patients (5 cures and 7 failures, Table S1) were subjected to RNA-seq and analyzed. The samples were evaluated for quality via a series of diagnostic plots of sequencing depth, genes observed with respect to depth, coefficients of variance, variance with respect to metadata factors, and sample distributions via correlation/distance heatmaps and PCA. The median number of reads obtained from these samples was 38,460,955, and the average percentage kept was 80% (Table S2). Following low coverage filtering, 2 of the 36 samples were removed for having less than 15,000 observed genes (Figure 4A). For the remaining 34 samples, we used a variance partitioning analysis to evaluate the effect of different factors (visit number, infecting species, drug, and clinical outcome) on the response measured (gene expression). The relative contributions to variance were expressed as violin plots (Figure S3A–B). For the majority of genes, the observed variance was not explained by any of the evaluated factors (visit number, infecting species, drug, and clinical outcome), highlighting the known effect of inter-donor variability in human samples. Among the evaluated variables and including all transcriptome samples together, neither the infecting species, nor the clinical outcome contributed a significant proportion of the variance in the data (Figure S3A). However, visit number (together with the donor) were the variables that accounted for most of the variance within the dataset (Figure S3B). This was corroborated by PCA, where transcriptomes from samples collected at all time points showed clustering by visit along principal component 1 (PC1), with a good separation between Pre-Tx samples, and those collected after treatment was initiated (i.e. Mid-Tx and EoTx samples) (Figure 4B). Consistent with the preliminary data collected by RT-qPCR, no separation between transcriptomes was observed when comparing treatment with GLUC or MLF (Figure S3C), cure or TF (Figure S3D), and infection with L. (V.) panamensis or L. (V.) braziliensis (Figure S3E). However, at Mid-Tx and EoTx, slight discrimination by species could be observed, in line with the higher reported reactogenicity of L. (V.) braziliensis (27). The sex factor was not explored in the variance analysis due to the low number of women included in the study (2 of 12). Although this could constitute a study limitation, the incidence of CL in the Americas is on average 20%. This is due to the occupational risk of exposure to infection in forest and agricultural areas, favoring infection in young adult men (28).

Figure 4. Assessment of quality of samples for RNA-seq.

Figure 4.

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Evaluation of sequencing depth per sample. Plot of reads mapped (sequencing depth) vs. number of genes detected. The plot includes a total of 36 samples collected and sequenced in the study (A). A principal component analysis (PCA) was performed using log2(CPM) values from biopsies with past QC (n=34, where 15 were from patients who cured, and 19 from TF). Biopsies from the same lesion were obtained before (Pre-Tx, green), at the middle (Mid-Tx, orange), and at the end of treatment (EoTx, purple) from all study participants (B).

Following up on the observation that the time at which the lesion biopsy was obtained (visit number) contributed to a significant proportion of the variance in the transcriptomic data, we analyzed the transcriptional changes (dynamics of gene expression) occurring during the course of treatment, and their relationship to cure and TF. A differential expression (DE) analysis for each outcome group (cures and TF) was conducted comparing Mid-Tx vs. Pre-Tx, EoTx vs. Pre-Tx and EoTx vs. Mid-Tx biopsies (Table S2). In cured patients, the number of significantly DE genes (DEGs) was the highest between Pre-Tx compared to either Mid-Tx or EoTx samples (874 and 1,487, respectively). Minimal transcriptional changes (16 DEGs) were observed between EoTx vs. Mid-Tx biopsies, suggesting that mechanisms of drug-induced CL healing stabilize as early as 10 days after initiation of treatment (Figure 5A). In contrast to patients who cured, patients with TF showed the most significant transcriptional changes between EoTx and Pre-Tx samples (1,849 DEGs); few DEGs were found between Mid-Tx vs. Pre-tx or EoTx vs. Mid-Tx (315 and 222, respectively) (Figure 5B). Among DEGs in Mid-Tx vs. Pre-Tx samples, only 97 genes were commonly modulated in cures and TF (Figure 5C). Patients who cured displayed a greater perturbation of the transcriptional profile (476 up- and 301 down-regulated genes in cures vs. 193 up- and 25 down-regulated in TF). By EoTx, the number of common genes modulated in cures and TF increased to 861 (Figure 5C), as did the number of unique DEGs in TF.

Figure 5. Differential expression analyses of lesion biopsies from CL patients during the course of treatment.

Figure 5.

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Volcano plot showing up-regulated (red) and down-regulated (blue) genes during the course of treatment in biopsies from patients who cured (A) or failed treatment (TF) (B). Cut-off lines along the X axis were defined at log2(FC)= |1|, and along the Y axis at -log10 adj P value ≤ 0.05. Biopsies from the same lesion were obtained before (Pre-Tx), in the middle (Mid-Tx), and at the end of treatment (EoTx). Comparison of DE gene numbers between cures and TF is shown as a Venn diagram (C).

Antileishmanial-mediated healing of skin lesions is mediated by the development of the stratum corneum and suppression of the inflammatory response

Genes that were uniquely differentially expressed in either cures or TF (in the contrast of Mid-Tx vs. Pre-Tx lesion transcriptomes), were used as input for gene enrichment analyses. In patients that cured, pathways involved in the formation of the cornified envelope, integrin-cell surface interactions, assembly of collagen fibrils, gap junction trafficking and cell junction organization were significantly enriched in up-regulated genes (Figure 6 and Table S3). Among the top ten over-expressed genes, three were functionally related to stratum corneum formation (krt37, krt13 and krt34) (Table S3). Among down-regulated genes, enriched pathways corresponded to neutrophil degranulation, IL10 and IFN signaling, metallothioneins, inflammasomes and T cell co-stimulation (Figure 6 and Table S3). These data suggest that drug-mediated healing of CL lesions is dependent upon early induction of development of the stratum corneum, suppression of T cell-mediated inflammatory responses, and damping of neutrophil activation. Towards EoTx in cured patients, the pathways of assembly of collagen fibrils, integrin cell surface interactions, and organization of the extracellular matrix were also enriched. All dowregulated pathways were related to the inflammatory response (Table S3).

Figure 6. Functional interaction network of DEGs between Pre-Tx and Mid-Tx skin biopsy samples in cured CL patients.

Figure 6.

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One hundred and six uniquely up-regulated genes in cured CL patients, and 301 down-regulated genes were significantly enriched in gene clusters. Networks were constructed in String V.11.5. Line thickness represents confidence of the interaction, set as 0.4 default. Red border in nodes represents up-regulated genes; blue borders, down-regulated genes. The intensity of the border color reflects the magnitude of the DE (log2 (FC)).

In TF patients, genes associated with keratinization and interferon signaling were down-regulated towards Mid-Tx. Notably, and in stark contrast with cured patients, within the top 10 dowregulated genes, 5 were related to the keratinization process (krtap9-3, krtap3-3, krtap24-, krtap17-1 and krtap19–1) (Table S3). Among overexpressed genes, B Cell receptor signaling, classical antibody-mediated complement, and FCGR activation were enriched (Table S3). At EoTx, pathways mainly associated with cell cycle and cell differentiation processes were dowregulated. In contrast, only two overexpressed pathways that were enriched (transcriptional regulation of white adipocyte differentiation and formation of the cornified envelope) in the contrast of EoTx vs. Pre-Tx (Table S3).

In order to observe the dynamics of gene expression during the course of the treatment, genes that were DE in at least one-time point between cures and TFs were selected (n=2801) for characterization of expression kinetics based on correlation analyses. Counts per million (CPM) were log-transformed and used as input for network analysis (19) with a parameter set to the Spearman |ρ|> 0.75. The Markov clustering algorithm was used for unsupervised clustering of gene expression datasets. Five clusters of co-expressed genes were formed (Figure 7). Cluster 1 contained the highest number of genes (n=753) (Figure 7A). For both study groups, the expression kinetics of these genes showed an increase towards the EoTx. However, the average expression of these genes during the course of treatment was greater in cures, as confirmed by DE analyses (Table S2). Pathways associated with this cluster were explored by reactome enrichment analysis and were mainly related to the development of extracellular matrix and collagen formation (Figure S4B–C). Cluster 2 was composed of 580 genes (Figure 7B), and the main enriched function was associated with stratum corneum formation. Cluster 2 genes showed increased expression in cures by Mid-Tx, and stabilizing by EoTx, while in TF, expression increased over time (Figure S4E). Contrary to what was observed in clusters 1 and 2, cluster 3 genes (Figure 7C) showed a gradual decrease in the expression towards the EoTx, with average higher gene expression in TF, corroborated by DE analyses of each gene (Table S2). Pathways enriched from this cluster were mainly associated with neutrophil degranulation and interferon signaling (Figure S4G–H). Cluster 4 was represented by genes involved in cell adhesion (Figure S4J, and Figure 7D). Finally, cluster 5 (Figure 7E) corresponded to genes related to cell cycle progression. Pre-Tx and Mid-Tx levels were higher in TF, showing an overall decrease by EoTx, as did in biopsies from cured patients (Figure S4L).

Figure 7. Gene expression networks in lesion biopsies from CL patients during antileishmanial treatment.

Figure 7.

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Network representation of co-expressed genes in lesion biopsies of CL patients (n = 12). Genes with correlated expression (Spearman’s correlation cutoff |ρ|> 0.75) during the course of treatment are shown. Average expression of all genes within each cluster is shown for all patients (n=12, black line), as well as for cures (n=5, purple line) and TF (n=7, orange line). The average expression for each gene, among all patients is represented by gray lines. Gene expression is shown as log2(CPM).

DISCUSSION

The resolution of cutaneous ulcers caused by Leishmania infection has been almost exclusively attributed to the reduction or elimination of the infecting parasite from the host. However, the relationship between parasite load, disease severity, and lesion resolution is not linear. This is illustrated by chronic CL ulcers caused by L. (Viannia) sp., which often have an undetectable parasite load (29). Most of our current understanding of the immunological features of CL lesions derive from cross-sectional studies with measurement at a single point in time. Nevertheless, healing of CL is clinically evaluated 3 to 6 months after end of standard-of-care treatments, indicating that mechanistic studies of wound healing should also be conducted over time. Currently, the knowledge of local (skin) mechanisms and pathways leading to healing of CL lesion is limited to an overall damping of the inflammatory response. However, the precise mechanisms and dynamics of these responses are unknown. This longitudinal study presents results investigating the clinical and molecular evolution of CL lesions during the chemotherapeutic intervention in patients, and its relationship with cure and TF. We characterized the transcriptional dynamics of skin lesions and identified host gene signatures and pathways associated with these divergent responses.

Multiple studies on risk factors in CL patients have highlighted an association between time of lesion evolution and the outcome of treatment. Consistent with previous reports, patients with TF had a shorter time of lesion evolution at the time of initiation of treatment (30–32) and the mechanism behind this clinical observation remains unknown. One possibility could be that the immune response has not reached an “optimal threshold” for control of the parasite load, before initiating the wound healing process. In line with this, patients with TF and infected by L. (V.) braziliensis had a higher parasite load compared to patients with an effective therapeutic response (8), and this was also found in our study cohort. Higher parasite loads may result in an environment that can lead to increased recruitment of innate immune cells (e.g. neutrophils and monocytes), leading to exacerbated inflammation and tissue damage. However, no linear relationship between parasite load, disease severity and therapeutic outcome has been successfully established (33). This is illustrated, for example, by patients with chronic CL caused by L. Viannia where parasite loads are almost undetectable at diagnosis (29), and by the observation that even in cured patients, parasites can be detected in scars, blood or mucosal tissues, indicating that the outcome of treatment is not dependent on parasite clearance (33–36). The size of CL lesions before initiation of treatment has not been associated with risk of TF, similar to what observed in our study participants. However, the ulcer size of patients with TF increased towards Mid-Tx, and larger lesions were found in patients infected with L. (V.) braziliensis. L. (V.) braziliensis infections have been shown to be more likely refractory to treatment, compared to other L. (Viannia) sp. (30). Together, these findings support a role for the parasite in modulating the immune response and evolution of CL lesions, where differences in the infecting species and the parasite load could contribute as triggers, but unlikely likely as the drivers of the outcome of antileishmanial treatment.

Damping of the inflammatory response is central to wound healing. As observed in our PCR-array screening and in the RNA-seq data, inhibition of genes related to inflammatory responses is observed mainly in skin samples from patients who cured. By Mid-Tx, TF patients showed increased expression of ccl2, cxcl3 and cxcl8, chemokines involved with monocyte and neutrophil recruitment, as well as genes involved in FCγR signaling and complement activation. This is consistent with a highly inflammatory environment driving the clinical evolution of the skin ulcer in TF, contrary to what was observed in the patients who cured. While early activation and recruitment of monocytes to the lesion promotes rapid parasite clearance for early control of infection, continued expression of these mediators during treatment may promote a pro-inflammatory and immunopathogenic response due to sustained phagocyte recruitment and activation.

There were large transcriptional changes comparing Mid-Tx vs Pre-Tx (more DEGs), and minimal variability in EoTx vs. Mid-Tx in transcriptomes from cures. This suggests that drug-induced healing mechanisms stabilize by Mid-Tx (day 10), although it is not phenotypically evident until EoTx (or even later at 3 or 6 months after treatment). Our results reveal significant and interesting transcriptional changes occurring in lesions as early as 10 days post-initiation of treatment, suggesting this as an ideal timepoint for intervention. The transcriptional profiles found in this study show that drug-mediated healing of CL lesions depends on activation of pathways related to stratum corneum development, cell surface interactions with integrins, organization of the extracellular matrix, and inhibition of pathways related to the inflammatory response such as suppression of the T cell-mediated response and damping of neutrophil activation. Thus, the resolution of a leishmaniasis lesion is analogous to wound healing. In the skin, wound repair requires interaction between different cell types: keratinocytes, fibroblasts, immune cells and endothelial cells. These interactions allow the restoration of the epithelial barrier through keratinocyte proliferation and migration, restoration of dermal architecture through extracellular matrix production, and myofibroblast-mediated wound contraction (37). Matrix metalloproteinases (MMP) activity is required in wound closure by keratinocyte re-epithelialization and migration and angiogenesis. The matrix metalloproteinase 13 (mmp13) was the top overexpressed gene at Mid-Tx in patients who cured. Previous studies have shown that MMP13 knock out mice had delayed wound healing (38). MMP13 promotes the growth and maturation of granulation tissue, including myofibroblastic function, inflammation, angiogenesis, and proteolysis (39). Gene co-expression analyses were implemented with the purpose of characterizing the dynamics of gene expression over the course of treatment, and to explore whether co-expressed genes could inform general functions modulated over time in our two study groups (cure and TF). Indeed, co-expressed genes were associated with specific tissue functions such extracellular matrix organization, cell cycle progression, among others. When comparing between groups, genes within clusters showed a similar directionality of expression during the course of treatment. However, patients who cured had significantly higher levels of expression at all timepoints compared to TF as supported by gene enrichment analysis and DE analyses, especially in co-expressed genes involved in stratum corneum formation and extracellular matrix organization. These findings corroborate a sustained impairment of wound healing in patients presenting with TF.

Overall, our results reveal that systemic treatment with GLUC and MLF leads to strong modulation of transcriptional signatures of skin-specific inflammation and tissue formation. Although the directionality of gene expression changes over treatment was similar in cures and TF, the magnitude of gene expression was consistenly and significantly different between the two study groups. Our study supports that the balance and coordinated dynamics between the inflammatory response and tissue remodeling are necessary for the healing process of CL lesions. Finally, this work highlights the consolidation of a molecular signature of healing as early as 10 days after initiation of treatment, suggesting that add-on or combinatory interventions targeting the inflammatory and/or wound healing responses should be conducted within the first 10 days of initiation of antileishmanial chemotherapy. This finding should guide future clinical studies and trials evaluating the efficacy of immunomodulators and antileishmanial-immunomodulator combination therapies.

Supplementary Material

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Key Points.

  • Antileishmanials modulate transcriptomic profiles of inflammation and skin remodeling

  • Sustained inflammation and impaired tissue remodeling lead to CL treatment failure

  • Major transcriptional changes in CL lesions occur at 10 days of treatment

ACKNOWLEDGMENTS

We gratefully acknowledge the patients who participated in this study and members of the clinical group of CIDEIM in Cali, Colombia, and Tumaco, Colombia, in the implementation and conduct of the research protocol.

DATA AVAILABILITY STATEMENT

All sequence data is publicly available in NCBI’s Short Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA978060) under bioproject PRJNA978060.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1954409-supplement-1.pdf (732.8KB, pdf)
2
NIHMS1954409-supplement-2.xlsx (9.6MB, xlsx)
3
NIHMS1954409-supplement-3.xlsx (38.8KB, xlsx)

Data Availability Statement

All sequence data is publicly available in NCBI’s Short Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA978060) under bioproject PRJNA978060.

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