1. Introduction
Balamuthia mandrillaris is a free-living amoeba (FLA) distributed worldwide and can be found in diverse environmental sources such as soil, freshwater, etc., [1]. This amoeba belongs to the genus of FLA that are pathogenic to humans, alongside Acanthamoeba, Naegleria fowleri, Vermamoeba vermiformis and Sappinia pedata [2,3]. B. mandrillaris has gained attention due to its ability to cause severe infections in humans, particularly granulomatous amoebic encephalitis (GAE), a condition that affects the central nervous system (CNS), causing severe neurological damage and skin lesions. Importantly, B. mandrillaris can infect both immunocompromised and immunocompetent individuals, highlighting its wide pathogenic potential [4,5]. Despite advancements in diagnostic techniques, infections caused by B. mandrillaris are frequently misdiagnosed or identified late due to their nonspecific symptoms and the rarity of the disease, with a global mortality rate > 95%. Reports of B. mandrillaris infections have increased worldwide, emphasizing the urgent need for novel approaches to prevent and treat this often-fatal infection [6,7].
In recent years, Mexican oregano (Lippia graveolens) has obtained significant interest for its broad-spectrum antimicrobial properties, demonstrating promising effects against bacteria [8], pathogenic fungi [9], and parasitic protozoa [10], making it a potential candidate for combating B. mandrillaris. Due to the lack of targeted and effective treatment options against GAE, there has been an interest in designing treatments based on plant-related compounds, as in the case of ursolic acid, betulinic acid, and botulin, described in Rinorea yaundensis and Salvia triloba, which have been proven to have amoebicidal activity against Naegleria fowleri, B. mandrillaris [11], and Acanthamoeba [12]. Garcinia mangostana compounds (9-hydroxycalabaxanthone, tovophillin A, garcinone E, garcinone B, α-mangostin, gartinin, 8-deoxygartinin, and γ-mangostin) also showed anti-Acanthamoeba activity [13], and pinocembrin, sakuranetin, cirsimaritin, and naringenin, flavonoids present in L. graveolens, revealed antiprotozoan activity against E. histolytica [10]. Nevertheless, no studies have reported on the genetic responses of B. mandrillaris, and the molecular machinery that this pathogen uses to defend itself against such compounds remains unknown, which represents an opportunity to identify potential targets that could lead to the development of effective treatments. In this sense, transcriptomic approaches can provide a powerful tool to investigate the cellular responses of B. mandrillaris to treatment with oregano extracts, allowing the identification of genes that show significant changes in their expression after treatment, providing a clear view of the cellular and molecular mechanisms involved. Importantly, L. graveolens has been proven to have no cytotoxic effects on human cells, making it a promising candidate for therapeutic use [14].
This study analyzed the transcriptomic response of B. mandrillaris to different L. graveolens extract fractions, revealing information on the transcriptional changes and the molecular mechanisms involved as a response. Given the urgent need for effective treatments against GAE, the differentially expressed genes identified could serve as valuable targets for further research. Understanding their role in diverse functions as a stress response and virulence may hopefully provide the basis for developing novel therapeutic strategies and advancing our knowledge of B. mandrillaris pathogenesis.
2. Materials and Methods
2.1. Culture, L. graveolens Fraction Exposure and Harvest of B. mandrillaris
Aerial parts (flowers, leaves, and small stems) of wild Lippia graveolens samples were collected in Durango, Mexico (coordinates N 23°32′43.8″ W 104°22′20.8″). After collection, L. graveolens taxonomic identification was carried out as reported by Gutierrez-Grivalja et al., 2019 [15]. L. graveolens samples were dried using Excalibur Food Dehydrator Parallax Hyperware (Sacramento, CA, USA) at a temperature of 40° C for 24 h. The dried samples were ground to a fine powder using a mill grinder, Ika Werke M20 (IKA, Wilmington, NC, USA). After that, samples were stored at −20 °C. Balamuthia mandrillaris (ITSON01 strain) was isolated from an artificial lagoon in Ciudad Obregon, Mexico, in 2014 [16]. The axenic culture was maintained using Balamuthia mandrillaris ITSON (BMI) medium, which consists of 10 g of Bacto™ Casitone (Difco, Detroit, MI, USA) dissolved in 500 mL of ddH2O, supplemented with 10% (v/v) fetal bovine serum, 200 UI/mL penicillin, and 200 μg/mL streptomycin. Additionally, 34 mL of Hank’s 10×balanced salt solution was included, containing CaCl2 (144 mg/L), MgCl2·6H2O (1000 mg/L), MgSO4·7H2O (1000 mg/L), KCl (4000 mg/L), KH2PO4 (600 mg/L), NaCl (80,000 mg/L), Na2HPO4·7H2O (900 mg/L), and D-glucose (10,000 mg/L) [16]. The culture was grown in 75 cm² flasks containing 9 mL of BMI medium and 1 mL of fetal bovine serum, incubated at 37 °C for one week. After incubation, two different fractions of L. graveolens were added to the flasks (3 per fraction), using B. mandrillaris in axenic medium (BMI) as a control. Fraction 1 (F1) (contained pure extract) and Fraction 2 (F2) (contained the extract diluted with 50% H2O). Each fraction had a 150 µg/mL concentration over three exposure times: 48, 96, and 120 h. Harvesting was carried out following the methodology of Lares-Jimenez et al. (2014) and Gonzalez-Zuñiga et al. (2024) [16,17].
2.2. Obtention of Lippia graveolens Extracts
Oregano extracts were obtained using 2 g of oregano sample mixed with 100 mL of ethanol; the mixture was incubated for 2 h in the absence of white light under agitation in Orbital Shaker Dos-10L (ELMI, San Jose, CA, USA) at 200 rpm. After that, the mixture was centrifuged at 10,000 rpm for 15 min at 4° C. The supernatant was collected and used in further studies. After mixing, the samples were centrifuged using a Hermle centrifuge model Z 36 HK (15 min, 10,000 rpm, 4 °C). All the supernatant liquid was then collected. To concentrate the samples, Buchi Syncore Plus coupled to Buchi Vacuum Pump V-300 was used (Buchi, St. Gallen, Switzerland).
Approximately 50 mL of each fraction was taken, and each fraction was poured into three tubes. The equipment was left to work while the coolant temperature was kept at 4 °C, the vacuum pump pressure was set at 22 mbar, the bath temperature was 40 °C, and these parameters were maintained for 3 h. At the end, the dried sample was resuspended with 10 mL of ethanol in each tube.
Obtention of Lippia graveolens Fractions
Strata SI-1 Silica (55 µm) 100 mg/1 mL tubes (Phenomenex, Torrance, CA, USA) were fractionated. The Strata SI-1 columns were conditioned with 5 mL of acetonitrile, and then the solvent was discarded. After discarding the acetonitrile again, the tubes were washed twice with 5 mL of ethanol, which was discarded.
For Fraction 1, 5 mL of pure extract was added to the Strata tube and the filtered sample was collected in a 50 mL Corning tube; this was performed twice. For Fraction 2, distilled water was used to obtain the extract in the tube. This process was performed in triplicate, obtaining approximately 30 mL per fraction. The fractions were kept at −18 °C in an ultra-freezer.
2.3. RNA Extraction and Sequencing
RNA extraction from B. mandrillaris exposed to L. graveolens extract fractions and control samples was performed using a combination of Invitrogen TRIzol Reagent and PROMEGA SV Total RNA Isolation System protocols [17]. A microtube with approximately 3.295 × 107 of harvested cells was considered a biological replicate, and three biological replicates per condition were used for RNA extraction and library preparation. The poly(A) libraries for each condition (F1, F2, and Control) were sequenced by Poly(A)RNAseq (150 bp Paired-End reads, 2 × 150 pb) through Illumina NovaSeq 6000 at the LC Sciences Laboratory (Houston, TX, USA). Fastq files can be found in the Sequence Read Archive of National Center for Biotechnology Information (NCBI), database ID: PRJNA1208996.
2.4. Bioinformatic Analysis
The quality of raw reads was analyzed through the FastQC tool (0.11.9 version) [18], and a graphical integration of all reports was carried out using MultiQC (1.13 version) [19]. Raw reads were filtered using Trimmomatic software (0.38 version) [20] with the following parameters: ILLUMINACLIP using the Truseq3 adapter sequence, with a minimum average quality score of 30 (SLIDINGWINDOW:4:30) and a minimum read length of 50 bp (MINLEN:50). Clean reads were aligned to the reference genome of B. mandrillaris strain ITSON01 [21] with STAR software (2.7.0 version) [22]. Htseq-count (0.9.1 version) was used for the quantification of the gene-mapped reads [23]. Raw counts were imported to the R/DESeq2 package (1.32.0 version) for exploratory and differential expression analysis. The matrix of raw counts was normalized by the variance-stabilizing transformation method to perform a principal component analysis (PCA) as a quality control step to identify atypical replicates and global expression patterns. Then, the matrix of raw counts was normalized by the median of ratios method to perform a differential expression analysis between treatments (L. graveolens fractions and different exposure times) and the control. Genes were identified as differentially expressed genes (DEGs) if their absolute value of Log2-fold change (FC) was >1 and their adjusted p-value was <0.01 [24]. DEGs were visually represented in volcano plots and heatmaps using the R packages EnhancedVolcano (1.24.00 version) and pheatmap (1.0.12 version). The R/GOseq package (1.44.0 version) was used to identify over-represented functional categories from the DEGs with a p-adj < 0.05 [25].
2.5. DEG Selection for Primer Design
Of all the DEGs, 10 genes were selected for primer design and validation using real-time quantitative PCR (RT-qPCR) (Table 1), including 7 up-regulated genes and 3 down-regulated genes, as identified in the differential expression analysis. Also, 2 genes (bal_000023 and bal_000159) were chosen as potential normalizers due to their stable expression across all tested conditions. Primer design for the RT-qPCR analysis was carried out through the PrimerQuest Tool (Integrated DNA Technologies, Coralville, IA, USA) (Table 1) [26].
2.6. cDNA Synthesis and Validation by RT-qPCR Analysis
For cDNA synthesis, the protocol for ImProm-II™ Reverse Transcription System (Promega®) was used, and oligo d(T)20 primers of the 10 genes previously selected (T4OLIGO, Irapuato, GTO, MEX) were used. The resulting cDNA was diluted with 30 μL of ultrapure water, and 5 μL was used as the template for RT-qPCR. Each RT-qPCR reaction was performed in triplicate (technical replicates) with a final reaction volume of 15 μL, prepared according to the instructions for GoTaq® Flexi DNA Polymerase (Promega®). The reaction mix included 0.35 μL of primers, 0.75 μL of EvaGreen® 20X (Biotium, Fremont, CA, USA), and 5 μL of cDNA. Amplification was carried out using the CFX Connect™ Real-Time PCR Detection System (BioRad, Hercules, CA, USA) under the following conditions: an initial denaturation at 95 °C for 5 min, followed by 39 cycles of denaturation at 95 °C for 15s, annealing for 30 s at 60 °C and extension at 72 °C for 30 s. Relative gene expression levels were calculated using the 2−ΔΔCT method, with the geometric mean of bal_000023 and bal_000159 as normalizer genes. The resulting data were normalized through log10+1 [27,28] and subsequently transformed to Log2-FC.
3. Results
3.1. Effect of L. graveolens Extract Fractions on B. mandrillaris
Fractions 1 (F1) and 2 (F2) of L. graveolens were observed to induce an encystment response in Balamuthia mandrillaris in all tested exposure times (48, 96, and 120 h) (Figure 1a,b). This response suggests that these fractions have the potential to trigger encystment, a survival mechanism in B. mandrillaris. Encystment is a well-documented strategy employed by B. mandrillaris in adverse conditions. Although the encystment induced by F1 and F2 in all samples was modest, the results show their capability to influence the cellular state of B. mandrillaris.
3.2. Analysis of Transcriptomic Data
The RNA sequencing analysis of B. mandrillaris exposed to different fractions of L. graveolens provided high-quality data for downstream bioinformatics analyses. The number of raw reads per sample ranged from 16.5 to 22.5 million reads, with that of filtered reads (post-quality control) varying between 11.8 and 16.4 million (Table S1). After filtering, a substantial proportion of reads were successfully mapped to the B. mandrillaris reference genome, with unique mapping rates ranging from 64.23% to 73.16%. This indicates a high specificity of the reads mapped to the reference genome [29].
3.3. Transcriptomic Response of B. mandrillaris to L. graveolens Extract Fractions
The transcriptomic analysis revealed significant changes in gene expression. The results highlight a dynamic response in B. mandrillaris to the treatments, varying with both the fraction and exposure duration. PCA of transcriptomic profiles of B. mandrillaris exposed to different L. graveolens fractions and control samples demonstrated a clear clustering pattern, indicating high reproducibility; consistency among biological replicates was used to observe global expression patterns. Interestingly, the results revealed clustering based on the exposure time, regardless of the fraction (Figure 2), supporting the validity of the experimental design and the differential response of B. mandrillaris to the tested fractions [30].
3.4. Differential Expression Analysis
Volcano plots were constructed to illustrate the differential gene expression profiles of Balamuthia mandrillaris exposed to Lippia graveolens F1 and F2 at 48, 96, and 120 h (Figure 3). These plots provide a clear visualization of DEGs, where up-regulated genes are positioned on the right and down-regulated genes on the left of each plot, based on statistical significance and fold-change values. Also, to analyze the global expression patterns across all conditions, heatmaps were generated (Figures S1–S6) to visualize gene expression relationships and clustering between samples.
The number of DEGs increased over time in both fractions, reflecting progressive transcriptional adaptation to the bioactive compounds present in L. graveolens. At 48 h, the transcriptional response was slightly stronger in F1, with 584 DEGs (330 up-regulated, 254 down-regulated) compared to 543 DEGs (303 up-regulated, 240 down-regulated) in F2 (Tables S2 and S3). However, at 96 h, the response intensified more in F1, with 2609 DEGs (1463 up-regulated, 1146 down-regulated), while F2 presented 2225 DEGs (1279 up-regulated, 946 down-regulated) (Tables S4 and S5). Interestingly, at 120 h, the number of DEGs peaked in F2 with 2770 DEGs (1649 up-regulated, 1121 down-regulated), surpassing F1, which showed 2539 DEGs (1390 up-regulated, 1149 down-regulated) (Tables S6 and S7). This suggests that F1 triggers a stronger initial transcriptional response, but F2 induces a more pronounced effect at later time points, possibly due to differences in the composition or bioavailability of active compounds.
To analyze the DEG distribution in F1 and F2 at the different exposure times, Venn diagrams were generated to identify common and unique DEGs between both fractions (Figure 4). At 48 h, 39% of DEGs were shared, with 33% unique to F1 and 28% exclusive to F2 (Table S8). However, at 96 h, the proportion of shared DEGs increased significantly to 57%, with 28% and 15% remaining unique to F1 and F2, respectively (Table S9). By 120 h, 59% of DEGs were shared, while 17% were unique to F1 and 24% to F2 (Table S10), reinforcing the trend of increasing similarity in gene expression patterns over time. This increasing similarity of DEGs suggests the time-dependent convergence of transcriptional responses, possibly reflecting common stress or defense mechanisms activated by both fractions.
3.5. Over-Representation Analysis of GO Categories
To better understand the functional implications of the transcriptional response, GO term analysis was performed on the common DEGs shared between Balamuthia mandrillaris exposed to Lippia graveolens fractions F1 and F2 at three different times: 48, 96, and 120 h. The common DEGs were classified into three principal GO categories: molecular function (MF), biological process (BP), and cellular component (CC). This allowed for a comprehensive characterization of the biological pathways and molecular mechanisms involved. To identify the biological functions over-represented in the dataset, an over-representation analysis (ORA) was conducted, using an adjusted p-value < 0.05 for statistical significance. This analysis facilitated the identification of GO terms among the shared DEGs across extract fractions at all time points, providing insights into the biological processes activated as a response.
At 48 h, the conditions showed 18 over-represented GO categories, with 10 MF terms including protein tyrosine kinase activity (GO:0004713), transmembrane signaling receptor activity (GO:0004888), ATP binding (GO:0005524), and oxidoreductase activity, acting on CH-OH group of donors (GO:0016614) only for down-regulated genes, protein kinase activity (GO:0004672), and GTP binding (GO:0005525). Five terms were related to BP, including the cell surface receptor signaling pathway (GO:0007166) and protein phosphorylation (GO:0006468), while microtubule (GO:0005874) and spindle (GO:0005819), for down-regulated genes, and membrane (GO:0016020) were the terms associated with CC, with all GO categories being identified in both up- and down-regulated DEGs (Figure 5a).
At 96 h, both fractions revealed a total of nine over-represented GO terms. In the MF category, two enriched GO terms were identified for both up- and down-regulated genes: transmembrane signaling receptor activity (GO:0004888) and ATP binding (GO:0005524). In the BP category, five over-represented GO terms were identified. For down-regulated genes, terms such as DNA replication initiation (GO:0006270), mitotic M phase (GO:0000087), mRNA splicing, via spliceosome (GO:0000398), branched-chain amino acid biosynthetic process (GO:0009082), and cell surface receptor signaling pathway (GO:0007166) were identified for both over-expressed and under-expressed genes. In the CC category, two enriched GO terms were identified: U4/U6 × U5 tri-snRNP complex (GO:0046540) for up-regulated and down-regulated expression levels and minichromosome maintenance protein (MCM) complex (GO:0042555) for down-regulated genes (Figure 5b).
At 120 h, for F1 and F2, common DEGs had 14 GO terms that were identified as significantly over-represented. In the MF category, seven enriched GO terms were identified: NAD+ binding (GO:0070403), transmembrane signaling receptor activity (GO:0004888), protein tyrosine kinase activity (GO:0004713), peroxidase activity (GO:0004601), NAD+ ADP-ribosyltransferase activity (GO:0003950) and heme binding (GO:0020037) and microtubule binding (GO:0008017). In the BP category, five GO terms were identified. For genes with down-regulated expression levels, the terms were DNA replication initiation (GO:0006270) and mitotic M phase (GO:0000087), and terms for up- and down-regulated genes included cell surface receptor signaling pathway (GO:0007166), cell cycle (GO:0007049) and microtubule-based movement (GO:0007018). In the CC category, two enriched GO terms were identified: membrane (GO:0016020) for both expression levels, and Homologous to AUgmin Subunits (HAUS) complex (GO:0070652) for down-regulated genes (Figure 5c).
3.6. RT-qPCR Validation of RNA-Seq Data
To confirm the reliability of the transcriptomic data obtained through RNA-Seq, a subset of 10 DEGs were selected for validation via RT-qPCR (Table 1). These genes were chosen based on their statistical significance, biological relevance, and differential expression patterns observed across conditions. The RT-qPCR results demonstrated expression trends consistent with those observed in RNA-Seq (Figure 6). Genes that were up-regulated in RNA-Seq also showed increased expression in RT-qPCR, while down-regulated genes exhibited a similar reduction in transcript levels, reinforcing the accuracy and reproducibility of the RNA-Seq data. The concordance between both techniques supports the transcriptomic analysis and validates the expression changes identified in response to L. graveolens fractions.
4. Discussion
A comparative analysis between F1 and F2 at 48, 96, and 120 h revealed that while some transcriptional responses were unique to each fraction, a set of conserved genes was consistently regulated over time. These genes were primarily linked to stress adaptation, membrane integrity, and virulence modulation, suggesting that B. mandrillaris engages similar molecular pathways to mitigate the effects of the bioactive compounds in both fractions. The principal functional categories identified included cellular stress response, oxidative damage repair, transmembrane signaling, metabolic regulation, and structural maintenance, among others. These transcriptional responses reinforces that exposure to L. graveolens triggers common adaptive mechanisms.
4.1. Functions of Top GO Categories
Among all the enriched GO terms identified in the analysis, several categories emerged as particularly significant for their impact on the amoeba cellular processes, particularly categories such as protein kinase activity (GO:0004672), ATP binding (GO:0005524), and protein phosphorylation (GO:0006468), highlighting alterations in signal transduction and regulatory mechanisms. Additionally, functions associated with membrane integrity, such as membrane (GO:0016020) and transmembrane signaling receptor activity (GO:0004888), suggest structural and signaling disruptions in cellular membranes. The identification of oxidoreductase activity (GO:0016614) and NAD+ ADP-ribosyltransferase activity (GO:0070403) underscores a cellular response to oxidative stress and DNA repair pathways triggered by L. graveolens. Furthermore, terms such as small GTPase-mediated signal transduction (GO:0007264) and cell surface receptor signaling pathways (GO:0007166) point to potential disruptions in cytoskeletal organization and cellular communication, further emphasizing the substantial impact of these compounds on critical biological processes in B. mandrillaris. These categories are implicated in different processes. GO:0004672 (protein kinase activity) and GO:0004713 (protein tyrosine kinase activity) are implicated in the enzymes catalyzing phosphorylation, critical for several signaling such as transduction and cellular regulation [31]. GO:0005524 (ATP binding) describes some of the ATP-dependent enzymes involved in processes that require energy [32]. GO:0006468 (protein phosphorylation) regulates cellular processes supported by protein kinases and phosphatases in the genome, which are regulatory processes of protein activity and interaction [33]. GO:0016020 (membrane) outlines proteins associated with the structure, transport, and signaling of membranes [34]. GO:0007264 (small GTPase-mediated signal transduction) encompasses pathways that utilize small GTPases as molecular switches for the regulation of cytoskeletal organization, cell proliferation, and differentiation [35]. GO:0016614 (oxidoreductase activity, acting on the CH-OH group of donors) refers to enzymes driving oxidoreductive reactions crucial for metabolism and energy production [36]. GO:0070403 (NAD+ binding) describes enzymes that are involved in diverse functions like DNA repair, stress responses, and the regulation of genes [37]. GO:0004888 (transmembrane signaling receptor activity) focuses on receptors with the function of extracellular signal transmission into cellular and membrane responses [38], while GO:0007166 (cell surface receptor signaling pathway) relates to sequences of molecular functions such as the binding of extracellular ligands to receptors on the cell surface and the regulation of downstream cellular activity [39].
4.2. Up-Regulated Genes in Stress Response and Adaptation
The global analysis of B. mandrillaris exposed to L. graveolens fractions revealed genes involved in several functions such as stress response, cell stability, and virulence-related factors. Among the up-regulated genes, ephrin type-A receptor 4a (EPHA4) (bal_004939) (log2FC: 3.36), a receptor tyrosine kinase that regulates interactions between cells and the extracellular matrix, influences cellular behavior through bidirectional signaling [40]; though it is not well described in microorganisms such as FLA, its expression might be related to B. mandrillaris’s capability to evade damage and maintain cellular integrity as a response induced by L. graveolens, but further studies are needed to confirm the specific role of EPHA4. Similarly, peroxiredoxin (bal_002453) (log2FC: 3.45) reduces oxidative stress and protects cells from peroxide damage, enhancing the survival of protozoans within hostile host environments, contributing to their virulence such as in Leishmania species, where peroxiredoxins are important for resisting oxidative bursts from host immune cells [41]. The PHB domain-containing protein (bal_009677) (Log2FC: 2.63) stabilizes mitochondrial proteins and regulates the cell cycle, supporting mitochondrial integrity and supporting processes such as degradation and stress response [42]. The putative ATP-dependent permease ADP1 (bal_017266) (log2FC: 2.01) is among the ATP-binding cassette (ABC) proteins that transport metabolites, including antifungal agents, making them crucial in antifungal resistance and vital for fungal cell viability [43]. The fibroblast growth factor receptor 3-like protein (bal_004934) (log2FC: 3.22) has an unclear role in microorganisms, but it has been described as a regulation factor of growth and linked to various genetic disorders, such as certain cancers [44]. Ditrans, polycis-polyprenyl diphosphate synthase (bal_016491) (log2FC: 2.55) is an enzyme that catalyzes the formation of long-chain polyprenyl diphosphates, which are crucial in the synthesis of intermediates such as dolichol, essential for the maintenance and function of cellular membranes in microorganisms [45].
4.3. Down-Regulated Genes in Structural and Virulence Functions
Several down-regulated genes are related to structural factors and virulence. Arf GTPase Arf1 (bal_014485) (log2FC: −3.52) is implicated in the secretion of virulence factors through specialized organelles assisting in the invasion of host cells by regulating the exocytosis of molecules necessary for host–cell entry [46], and it has been reported in another pathogenic amoeba (E. histolytica) [47]. EhGATA has also been reported in E. histolytica as a regulator of genes involved in phagocytosis [48]; this transcription factor is related to the GATA-type zinc finger (bal_013716) (log2FC: −4.32), a DNA binding protein that plays a crucial role in the development and cell differentiation [49]. The alanine-phosphoribitol ligase (bal_023111; bal_015293) (log2FC: −4.53; −2.36) catalyzes the biosynthesis of phosphoglycans, which are essential for cell wall integrity and immune evasion in pathogenic microorganisms, and acts as a virulence factor [50]. Dicer-like protein (bal_018694) (log2FC: −2.55) was also identified as a down-regulated gene. This protein has been reported as directly involved in the fungal pathogenicity of Colletotrichum gloeosporioides [51]. M-phase inducer phosphatase (bal_019993) (log2FC: −2.36) was also identified as being essential for cell cycle progression, and its repression may lead to cell cycle arrest [52]. G-protein-coupled receptors (bal_023829) (log2FC: −2.37), revealed as down-regulated genes, regulate a variety of processes like cell proliferation [53]. Also, some hypothetical proteins such as bal_014161 (log2FC: −4.16) have also been identified, requiring further structural and functional characterization, but have also been found to be related to membrane activity.
5. Conclusions
The exposure of Balamuthia mandrillaris to different extract fractions of Lippia graveolens induced significant transcriptional changes, demonstrating its responses and adaptation to stress conditions. The differential gene expression analysis revealed a progressive increase in the number of DEGs over time, suggesting a time-dependent transcriptional response as B. mandrillaris adjusted its molecular pathways to counteract the effects of the treatment. A notable quantity of common DEGs was observed between the analyzed fractions, indicating that F1 and F2 activated shared adaptive mechanisms despite compositional differences. However, unique genes were also present in each fraction, suggesting that specific compounds may have distinct molecular effects. The GO analysis also revealed terms related to oxidoreductase activity, transmembrane signaling, protein phosphorylation, ATP-binding proteins, and DNA replication that highlight the molecular pathways induced by L. graveolens, which are essential for B. mandrillaris stability. Among the DEGs identified, genes associated with stress response, oxidative damage repair, membrane integrity, metabolic regulation, and virulence were significantly affected. The presence of virulence-associated genes among the DEGs suggests that L. graveolens may affect factors involved in B. mandrillaris’ ability to persist in hostile environments and evade host immune responses. This study provides new insights into the molecular mechanisms of B. mandrillaris adaptation and survival when exposed to bioactive plant compounds, reinforcing the potential of L. graveolens as a source of anti-pathogenic agents. Understanding these transcriptional responses lays the groundwork for future research to identify specific molecular targets and develop novel therapeutic strategies to combat infections caused by B. mandrillaris.
Conceptualization, L.Z.R.-A. and E.P.G.-G.; methodology, L.Z.R.-A., A.C.-M., E.P.G.-G. and J.R.G.-G.; software, L.D.G.-Z.; validation, L.Z.R.-A., J.R.G.-G. and A.C.-M.; formal analysis, L.D.G.-Z., A.C.-M., L.Z.R.-A., J.R.G.-G., F.L.V., J.L.-C. and D.I.S.-M.; investigation, L.D.G.-Z. and L.Z.R.-A.; resources, L.Z.R.-A.; writing—original manuscript, L.D.G.-Z., A.C.-M. and L.Z.R.-A.; writing—review and editing, L.Z.R.-A., F.L.V., J.R.G.-G., L.F.L.-J., D.I.S.-M. and J.L.-C.; supervision, L.Z.R.-A., A.C.-M. and J.R.G.-G.; project administration, L.Z.R.-A.; funding acquisition, L.Z.R.-A. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
All relevant data can be found in the article.
We thank the Instituto Tecnológico de Sonora (ITSON) for its support for our experimental analysis, as well as SECIHTI.
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
Footnotes
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Enlarge this image.Figure 1. Balamuthia mandrillaris exposed to fractionated compounds of Lippia graveolens at a concentration of 150 µg/mL for three exposure times. Cells were visualized at a 100× magnification. (a) Fraction 1 (F1) at 48 h, 96 h, and 120 h. (b) Fraction 2 (F2) at 48 h, 96 h, and 120 h.
Enlarge this image.Figure 2. Principal component analysis of B. mandrillaris transcriptomic profiles in response to L. graveolens fractions at different times. C_0d: control samples. F1_2d: Fraction 1 at 48 h. F1_4d: Fraction 1 at 96 h. F1_5d: Fraction 1 at 120 h. F2_2d: Fraction 2 at 48 h. F2_4d: Fraction 2 at 96 h. F2_5d: Fraction 2 at 120 h.
Enlarge this image.Figure 3. Volcano plot of differential gene expression in B. mandrillaris exposed to L. graveolens extract fractions. Significantly up-regulated genes are represented as green dots on the right, while down-regulated genes appear as green dots on the left. (a) Fraction 1 at 48 h. (b) Fraction 1 at 96 h. (c) Fraction 1 at 120 h. (d) Fraction 2 at 48 h. (e) Fraction 2 at 96 h. (f) Fraction 2 at 120 h.
Enlarge this image.Figure 4. Venn diagram of common and unique DEGs of B. mandrillaris exposed to L. graveolens extract fractions. (a) Fraction 1 and 2 at 48 h. (b) Fraction 1 and 2 at 96 h. (c) Fraction 1 and 2 at 120 h.
Enlarge this image.Figure 5. Over-presented GO terms in B. mandrillaris exposed to L. graveolens. (a) Fraction 1 and 2 at 48 h. (b) Fraction 1 and 2 at 96 h. (c) Fraction 1 and 2 at 120 h. Dot size represents the number of DEGs of each GO category and color indicates the adjusted p-values.
Enlarge this image.Figure 6. Validation of expression levels of B. mandrillaris exposed to L. graveolens fractions. Blue = RT-qPCR. Orange = RNA-Seq.
DEGs selected for primer design and qPCR validation.
| Gene ID | Protein | Log2-FC | Condition | Sequence Name | Sequence 5’–3’ |
|---|---|---|---|---|---|
| bal_000023 | Lipopolysaccharide-induced tumor necrosis factor alpha factor (LITAF) domain-containing protein | N/A | Normalizer | BAL1F | CGCTGTCACCATCAACAAAC |
| BAL1R | GCAGAGGCATAGGTGATGAG | ||||
| bal_000159 | Deacetylase sirtuin-type domain-containing protein | N/A | Normalizer | BAL2F | TGAGGAGAGATCAGGTGAAGAA |
| BAL2R | CAGCACTCTATCCGGCATTT | ||||
| bal_004939 | Ephrin type-A receptor 4a | 3.36 | F1_48 h | BAL7F | CCTTCTTCCATGTCCATCTCTC |
| BAL7R | GGTGTGTTGCTGGTACTTCT | ||||
| bal_014161 | Hypothetical protein | −4.16 | F1_48 h | BAL11F | ACAAGGTGGGAGCGTAGATA |
| BAL11R | CGGCGATTGTGGATTTGATTAC | ||||
| bal_002453 | Peroxiredoxin | 3.45 | F1_96 h | BAL12F | GCCATCCTCATAGTGGAAGTT |
| BAL12R | GTCGTGAGTTCAGGAGCATAG | ||||
| bal_009677 | Prohibitin (PHB) domain-containing protein | 2.63 | F1_120 h | BAL18F | ATGTCGGCTATGCGGTAGTA |
| BAL18R | CCAGGTTAGGACAATGGACTTG | ||||
| bal_017266 | Putative ATP-dependent permease ADP1 | 2.01 | F2_48 h | BAL26F | GCCGTGTTGCTGCTATTATG |
| BAL26R | GCTGGTCTTTCCTCCTCTTATG | ||||
| bal_014485 | ADP ribosylation factor (Arf) GTPase Arf1 | −3.52 | F2_48 h | BAL28F | GGATGAACCACTTCCTCTCTTT |
| BAL28R | GGAGACTGCCGTGTTAGTTT | ||||
| bal_023111 | Alanine-phosphoribitol ligase | −4.53 | F2_48 h | BAL29F | CATCACCTTGGCGTTCTCTC |
| BAL29R | CCAGCGTCAAACCACTTACA | ||||
| bal_004934 | Fibroblast growth factor receptor 3-like | 3.22 | F2_96 h | BAL31F | CATCGAGGTGGACAGTTATGAG |
| BAL31R | GTCAGAGTTAAGGTCCGTCAAG | ||||
| bal_016491 | Ditrans, polycis-polyprenyl diphosphate synthase | 2.55 | F2_120 h | BAL37F | TCCTCTGTAGTCGGGAATGT |
| BAL37R | GGCTCAGTGGATGAAAGAGAG | ||||
| bal_017433 | Wingless-related integration site (Wnt)-activated receptor activity | 3.61 | F2_120 h | BAL38F | CCGAGGTTGATGGATCGTATTG |
| BAL38R | TCTCTCCTTCCAGATCCACTTC |
Supplementary Materials
The following supporting information can be downloaded at:
References
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Details
; Cruz-Mendívil, Abraham 3
; Fernando Lares Villa 4 ; Gutiérrez-Grijalva, Erick Paul 5
; López-Cervantes, Jaime 6 ; Sánchez-Machado, Dalia I 6 ; Lares-Jiménez, Luis Fernando 4
; Libia Zulema Rodriguez-Anaya 2
1 Doctorate Program in Sciences Specialty in Biotechnology, Department of Biotechnology and Food Sciences, Instituto Tecnológico de Sonora, Ciudad Obregón 85000, Sonora, Mexico;
2 Investigadoras e Investigadores por México-Instituto Tecnológico de Sonora, Ciudad Obregón 85000, Sonora, Mexico;
3 Investigadoras e Investigadores por México-Instituto Politécnico Nacional, Centro Interdisciplinario de Investigación Para el Desarrollo Integral Regional Unidad Sinaloa, Guasave 81000, Sinaloa, Mexico;
4 Departamet of Agronomic Sciences and Veterinary, Instituto Tecnológico de Sonora, Ciudad Obregón 85000, Sonora, Mexico;
5 Investigadoras e Investigadores por México-Centro de Investigación en Alimentación y Desarrollo, AC., Culiacán 85480, Sinaloa, Mexico;
6 Department of Biotechnology and Food Sciences, Instituto Tecnológico de Sonora, Ciudad Obregón 85000, Sonora, Mexico;