1. Bibliographic Information

1.1. Title

Screening and transcriptomic analysis of anti-Sporothrix globosa targeting AbaA

1.2. Authors

Ying Wang, Xiaoyan Wu, Xiyuan Fan, Chanxu Han, Fangliang Zheng, and Zhenying Zhang.

• Affiliations: Academy of Life Science, Liaoning University (China); Department of Dermatology, University of Hong Kong Shenzhen Hospital (China); Department of Dermatology, The Eighth Affiliated Hospital, Sun Yat-sen University (China).

1.3. Journal/Conference

Frontiers in Microbiology

• Venue Reputation: This is a reputable open-access journal in the field of microbiology, known for publishing research on the interaction between microbes and hosts, as well as antimicrobial drug discovery.

1.4. Publication Year

2025 (Published online: April 29, 2025)

1.5. Abstract

This paper addresses Sporotrichosis, a fungal disease caused by Sporothrix species (S. schenckii and S. globosa), which causes chronic skin infections. The authors identify the gene abaA as a key drug target. Using virtual screening, they identified two FDA-approved small-molecule drugs, Azelastine and Mefloquine, which effectively inhibit these fungi. The study validates these findings through in vitro growth assays and in vivo mouse models. Transcriptomic analysis reveals that abaA regulates crucial virulence factors like cell wall attachment and melanin production, suggesting these repurposed drugs disrupt these pathways.

1.6. Original Source Link

/files/papers/6959d82c5411c3e2652eaee4/paper.pdf (Open Access)

---

2. Executive Summary

2.1. Background & Motivation

• The Problem: Sporotrichosis is a stubborn fungal infection affecting the skin and subcutaneous tissues of humans and animals. It is primarily caused by the Sporothrix complex. While treatments exist (e.g., Itraconazole), they often require long durations (months), and drug resistance is becoming a serious issue.
• The Gap: Sporothrix globosa is a major pathogen in certain regions (like Northeast China) but is under-researched compared to S. schenckii. There is an urgent need for new therapeutic options that are safe, effective, and faster-acting.
• The Innovation: The authors focus on Drug Repurposing (finding new uses for existing drugs) rather than developing new compounds from scratch. They target a specific gene, abaA, which acts as a "dimorphic switch"—controlling the fungus's ability to change from a harmless environmental form to a pathogenic form in the host.

2.2. Main Contributions / Findings

1. Target Identification: Validated the abaA gene as a critical target for controlling Sporothrix virulence.

2. Drug Discovery: Identified Azelastine (an antihistamine) and Mefloquine (an antimalarial) as effective antifungal agents against S. globosa and S. schenckii through virtual screening and experimental validation.

3. Mechanism Elucidation: Through transcriptomics (study of RNA expression), they demonstrated that targeting abaA disrupts genes responsible for cell wall integrity, melanin synthesis (a defense mechanism), and host attachment, while triggering stress repair pathways in the fungus.

4. In Vivo Efficacy: Validated that these drugs reduce skin lesions and inflammation in infected mice.

   ---

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand this paper, a beginner needs to grasp these concepts:

• Dimorphic Fungus: Fungi that can exist in two forms based on temperature.
  • Mycelial Phase ($25^\circ\text{C}$): Mold-like structure found in soil/environment (non-pathogenic).
  • Yeast Phase ($37^\circ\text{C}$): Single-celled form found in the host body (pathogenic). The transition from mycelium to yeast is called the Dimorphic Switch, and blocking this switch can stop infection.
• Transcription Factor (TF): A protein that binds to specific DNA sequences to control the "on" or "off" status of other genes. AbaA is a TF.
• Molecular Docking: A computer simulation technique used to predict how a small molecule (drug) interacts with a protein (target). It calculates the "binding energy"—the lower the energy, the tighter and more stable the binding.
• MIC & MBC:
  • MIC (Minimum Inhibitory Concentration): The lowest concentration of a drug needed to stop visible growth of the fungus.
  • MBC (Minimum Bactericidal Concentration): The lowest concentration needed to actually kill the fungus.
• Transcriptome / RNA-Seq: A method to measure the expression levels of all genes in an organism at a specific moment. It helps researchers see which genes are turned up (upregulated) or down (downregulated) in response to a drug.

3.2. Previous Works

• Role of abaA: The authors cite works (e.g., Andrianopoulos & Timberlake, 1994; Borneman et al., 2000) showing that abaA regulates asexual development in fungi like Aspergillus nidulans and Penicillium marneffei. In Sporothrix, it is hypothesized to control the critical switch to the pathogenic yeast phase.
• Current Treatments: The paper notes that standard treatments involve antifungal drugs like terbinafine and itraconazole, but resistance is noted (Zhang et al., 2024).

3.3. Differentiation Analysis

• Traditional Approach: Usually involves screening libraries of random chemical compounds, which is expensive and slow.

• This Paper's Approach: Uses Structure-Based Virtual Screening on an FDA-approved drug library. This allows for "Drug Repurposing," meaning the safety profiles of the candidates (Azelastine and Mefloquine) are already known, significantly speeding up potential clinical application. They uniquely combine this computational approach with wet-lab transcriptomic validation to map the specific genetic pathways affected by the drugs.

  ---

4. Methodology

4.1. Principles

The study follows a "Target-to-Drug" pipeline.

1. Target: The AbaA protein (essential for fungal development).
2. Screening: Computer algorithms predict which existing drugs bind to AbaA.
3. Validation: Test the drugs on fungi in test tubes (in vitro) and mice (in vivo).
4. Mechanism: Sequence RNA to see how the drugs affect fungal genetics.

4.2. Core Methodology In-depth

Step 1: Virtual Screening & Molecular Docking

Since the 3D structure of the S. globosa AbaA protein was unknown, the authors constructed it computationally.

1. Homology Search: They searched for proteins with similar sequences to AbaA using BLAST. They identified a protein (HMPREF1624) with a TEA/ATTS DNA-binding domain, a conserved region crucial for function.
2. 3D Modeling: Used Robetta (a protein structure prediction server) to generate a 3D model of the AbaA protein.
3. Pocket Prediction: Used DoGSiteScorer to identify the "binding pocket"—the specific crevice on the protein surface where a drug molecule could fit.
4. Docking: Used AutoDock Vina to simulate the binding of small molecules from an FDA-approved drug database into this pocket.

   • Selection Criteria: Candidates were ranked by Binding Energy (lower is better), price, and safety profile.

     The following figure (Figure 1 from the original paper) visualizes this process: (A) The predicted 3D structure of the AbaA protein. (F) The predicted binding pockets where drugs can attach. (G-H) The chemical structures of the identified drugs, Azelastine and Mefloquine, docked into the protein.

[Image of 1.jpg]

Step 2: In Vitro Antifungal Assays

To confirm the computer predictions, they tested the drugs on actual fungi.

1. Growth Curve Measurement:
   • Fungi were cultured in liquid medium.
   • Drugs (Azelastine/Mefloquine) were added at $50\ \mu\text{g/mL}$.
   • Growth was monitored by measuring Optical Density at 625 nm ($\text{OD}_{625}$). Higher OD means more fungal growth.
2. MIC and MBC Determination:

   • Performed via serial dilution in 96-well plates.

   • MIC: Visual inspection of the well with the lowest drug concentration that shows no growth.

   • MBC: Culturing liquid from clear wells onto agar plates to see if the fungus is dead (no regrowth).

     The following figure (Figure 2 from the original paper) shows the visual results of these assays. You can see the clear inhibition in the drug-treated groups compared to the control.

Figure 3 (below) provides the quantitative growth curves, showing a significant reduction in $\text{OD}_{625}$ (growth) over time for the treated groups.

Step 3: In Vivo Murine Model

To test if the drugs work in a living organism.

1. Modeling: Male KM mice were immunosuppressed (using cortisol) to make them susceptible. They were injected intradermally with Sporothrix spores.
2. Treatment: Mice were divided into groups:
   • Control: No drug.
   • Positive Control: Itraconazole (standard drug).
   • Experimental: Azelastine (low/high dose) and Mefloquine (low/high dose).
3. Histology: Skin lesions were excised, stained with Hematoxylin and Eosin (HE), and examined for granuloma width (size of the inflammatory nodule) and inflammatory cell infiltration.

Step 4: Transcriptomics & qRT-PCR

To understand the genetic mechanism.

1. RNA Sequencing (RNA-Seq): RNA was extracted from fungi in the Mycelial Phase (MP), Yeast Phase (YP), and Yeast Phase treated with Azelastine (YPA).
2. Differential Expression Analysis: They compared gene expression between phases and treatments.
3. Validation (qRT-PCR): They used quantitative Real-Time PCR to verify the sequencing results.
   • Mathematical Principle: The relative expression levels of genes were calculated using the $2^{-\Delta\Delta \text{Ct}}$ method.
   • Formula: $$\text{Relative Expression} = 2^{-\Delta\Delta \text{Ct}}$$
   • Symbol Explanation:

     • $\text{Ct}$ (Cycle Threshold): The number of cycles required for the fluorescent signal to cross the threshold.

     • $\Delta \text{Ct} = \text{Ct}_{\text{target gene}} - \text{Ct}_{\text{reference gene}}$ (Normalizes the target gene against a housekeeping gene like 18S rDNA).

     • $\Delta\Delta \text{Ct} = \Delta \text{Ct}_{\text{experimental group}} - \Delta \text{Ct}_{\text{control group}}$ (Compares the normalized expression of the experiment against the control).

       ---

5. Experimental Setup

5.1. Datasets & Materials

• Fungal Strains: Sporothrix globosa and Sporothrix schenckii (maintained at Liaoning University).
• Drug Library: FDA-approved small molecule database used for virtual screening.
• Animals: 8-week-old male KM mice (a common outbred mouse stock used in general research).

5.2. Evaluation Metrics

• Binding Energy: Measured in kcal/mol. Represents the strength of the interaction between the drug and the AbaA protein. More negative values indicate stronger binding.
• Optical Density ($\text{OD}_{625}$): Measures the turbidity of the liquid culture. $$\text{Absorbance} = -\log_{10} \left( \frac{I}{I_0} \right)$$
  • Where $I$ is the intensity of light passing through the sample and $I_0$ is the initial light intensity. High turbidity (high OD) implies high bacterial/fungal count.
• Granuloma Width: Measured in micrometers ($\mu\text{m}$). A granuloma is a collection of immune cells that forms a "wall" around the infection. Smaller width generally indicates successful treatment or containment.
• Gene Expression (TPM): Transcripts Per Million. A normalization method for RNA-Seq data to allow comparison between samples.

5.3. Baselines

• Negative Control: DMSO (solvent) or untreated medium. Used to ensure the solvent itself isn't toxic.

• Positive Control: Itraconazole. This is the current "gold standard" clinical treatment for Sporothrix. The new drugs (Azelastine/Mefloquine) are compared against this to see if they are as effective.

  ---

6. Results & Analysis

6.1. Virtual Screening Results

From the FDA library, four drugs were initially selected. Based on efficacy, price, and side effects, the authors narrowed it down to two:

• Azelastine

• Mefloquine

  Two other drugs, Avodart and Eltrombopag, were tested but showed no effect (see Figure 2A, B in the paper).

6.2. In Vitro Efficacy (MIC/MBC)

Both drugs showed strong inhibitory activity.

The following are the results from Table 4 of the original paper:. Bibliographic Information

1.1. Title

Screening and transcriptomic analysis of anti-Sporothrix globosa targeting AbaA

1.2. Authors

Ying Wang, Xiaoyan Wu, Xiyuan Fan, Chanxu Han, Fangliang Zheng, and Zhenying Zhang.

• Affiliations: Academy of Life Science, Liaoning University (China); Department of Dermatology, University of Hong Kong Shenzhen Hospital (China); Department of Dermatology, The Eighth Affiliated Hospital, Sun Yat-sen University (China).

1.3. Journal/Conference

Frontiers in Microbiology

• Venue Reputation: This is a reputable open-access journal in the field of microbiology, known for publishing research on the interaction between microbes and hosts, as well as antimicrobial drug discovery.

1.4. Publication Year

2025 (Published online: April 29, 2025)

1.5. Abstract

This paper addresses Sporotrichosis, a fungal disease caused by Sporothrix species (S. schenckii and S. globosa), which causes chronic skin infections. The authors identify the gene abaA as a key drug target. Using virtual screening, they identified two FDA-approved small-molecule drugs, Azelastine and Mefloquine, which effectively inhibit these fungi. The study validates these findings through in vitro growth assays and in vivo mouse models. Transcriptomic analysis reveals that abaA regulates crucial virulence factors like cell wall attachment and melanin production, suggesting these repurposed drugs disrupt these pathways.

1.6. Original Source Link

/files/papers/6959d82c5411c3e2652eaee4/paper.pdf (Open Access)

---

2. Executive Summary

2.1. Background & Motivation

• The Problem: Sporotrichosis is a stubborn fungal infection affecting the skin and subcutaneous tissues of humans and animals. It is primarily caused by the Sporothrix complex. While treatments exist (e.g., Itraconazole), they often require long durations (months), and drug resistance is becoming a serious issue.
• The Gap: Sporothrix globosa is a major pathogen in certain regions (like Northeast China) but is under-researched compared to S. schenckii. There is an urgent need for new therapeutic options that are safe, effective, and faster-acting.
• The Innovation: The authors focus on Drug Repurposing (finding new uses for existing drugs) rather than developing new compounds from scratch. They target a specific gene, abaA, which acts as a "dimorphic switch"—controlling the fungus's ability to change from a harmless environmental form to a pathogenic form in the host.

2.2. Main Contributions / Findings

1. Target Identification: Validated the abaA gene as a critical target for controlling Sporothrix virulence.

2. Drug Discovery: Identified Azelastine (an antihistamine) and Mefloquine (an antimalarial) as effective antifungal agents against S. globosa and S. schenckii through virtual screening and experimental validation.

3. Mechanism Elucidation: Through transcriptomics (study of RNA expression), they demonstrated that targeting abaA disrupts genes responsible for cell wall integrity, melanin synthesis (a defense mechanism), and host attachment, while triggering stress repair pathways in the fungus.

4. In Vivo Efficacy: Validated that these drugs reduce skin lesions and inflammation in infected mice.

   ---

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand this paper, a beginner needs to grasp these concepts:

• Dimorphic Fungus: Fungi that can exist in two forms based on temperature.
  • Mycelial Phase ($25^\circ\text{C}$): Mold-like structure found in soil/environment (non-pathogenic).
  • Yeast Phase ($37^\circ\text{C}$): Single-celled form found in the host body (pathogenic). The transition from mycelium to yeast is called the Dimorphic Switch, and blocking this switch can stop infection.
• Transcription Factor (TF): A protein that binds to specific DNA sequences to control the "on" or "off" status of other genes. AbaA is a TF.
• Molecular Docking: A computer simulation technique used to predict how a small molecule (drug) interacts with a protein (target). It calculates the "binding energy"—the lower the energy, the tighter and more stable the binding.
• MIC & MBC:
  • MIC (Minimum Inhibitory Concentration): The lowest concentration of a drug needed to stop visible growth of the fungus.
  • MBC (Minimum Bactericidal Concentration): The lowest concentration needed to actually kill the fungus.
• Transcriptome / RNA-Seq: A method to measure the expression levels of all genes in an organism at a specific moment. It helps researchers see which genes are turned up (upregulated) or down (downregulated) in response to a drug.

3.2. Previous Works

• Role of abaA: The authors cite works (e.g., Andrianopoulos & Timberlake, 1994; Borneman et al., 2000) showing that abaA regulates asexual development in fungi like Aspergillus nidulans and Penicillium marneffei. In Sporothrix, it is hypothesized to control the critical switch to the pathogenic yeast phase.
• Current Treatments: The paper notes that standard treatments involve antifungal drugs like terbinafine and itraconazole, but resistance is noted (Zhang et al., 2024).

3.3. Differentiation Analysis

• Traditional Approach: Usually involves screening libraries of random chemical compounds, which is expensive and slow.

• This Paper's Approach: Uses Structure-Based Virtual Screening on an FDA-approved drug library. This allows for "Drug Repurposing," meaning the safety profiles of the candidates (Azelastine and Mefloquine) are already known, significantly speeding up potential clinical application. They uniquely combine this computational approach with wet-lab transcriptomic validation to map the specific genetic pathways affected by the drugs.

  ---

4. Methodology

4.1. Principles

The study follows a "Target-to-Drug" pipeline.

1. Target: The AbaA protein (essential for fungal development).
2. Screening: Computer algorithms predict which existing drugs bind to AbaA.
3. Validation: Test the drugs on fungi in test tubes (in vitro) and mice (in vivo).
4. Mechanism: Sequence RNA to see how the drugs affect fungal genetics.

4.2. Core Methodology In-depth

Step 1: Virtual Screening & Molecular Docking

Since the 3D structure of the S. globosa AbaA protein was unknown, the authors constructed it computationally.

1. Homology Search: They searched for proteins with similar sequences to AbaA using BLAST. They identified a protein (HMPREF1624) with a TEA/ATTS DNA-binding domain, a conserved region crucial for function.
2. 3D Modeling: Used Robetta (a protein structure prediction server) to generate a 3D model of the AbaA protein.
3. Pocket Prediction: Used DoGSiteScorer to identify the "binding pocket"—the specific crevice on the protein surface where a drug molecule could fit.
4. Docking: Used AutoDock Vina to simulate the binding of small molecules from an FDA-approved drug database into this pocket.

   • Selection Criteria: Candidates were ranked by Binding Energy (lower is better), price, and safety profile.

     The following figure (Figure 1 from the original paper) visualizes this process: (A) The predicted 3D structure of the AbaA protein. (F) The predicted binding pockets where drugs can attach. (G-H) The chemical structures of the identified drugs, Azelastine and Mefloquine, docked into the protein.

[Image of 1.jpg]

Step 2: In Vitro Antifungal Assays

To confirm the computer predictions, they tested the drugs on actual fungi.

1. Growth Curve Measurement:
   • Fungi were cultured in liquid medium.
   • Drugs (Azelastine/Mefloquine) were added at $50\ \mu\text{g/mL}$.
   • Growth was monitored by measuring Optical Density at 625 nm ($\text{OD}_{625}$). Higher OD means more fungal growth.
2. MIC and MBC Determination:

   • Performed via serial dilution in 96-well plates.

   • MIC: Visual inspection of the well with the lowest drug concentration that shows no growth.

   • MBC: Culturing liquid from clear wells onto agar plates to see if the fungus is dead (no regrowth).

     The following figure (Figure 2 from the original paper) shows the visual results of these assays. You can see the clear inhibition in the drug-treated groups compared to the control.

Figure 3 (below) provides the quantitative growth curves, showing a significant reduction in $\text{OD}_{625}$ (growth) over time for the treated groups.

Step 3: In Vivo Murine Model

To test if the drugs work in a living organism.

1. Modeling: Male KM mice were immunosuppressed (using cortisol) to make them susceptible. They were injected intradermally with Sporothrix spores.
2. Treatment: Mice were divided into groups:
   • Control: No drug.
   • Positive Control: Itraconazole (standard drug).
   • Experimental: Azelastine (low/high dose) and Mefloquine (low/high dose).
3. Histology: Skin lesions were excised, stained with Hematoxylin and Eosin (HE), and examined for granuloma width (size of the inflammatory nodule) and inflammatory cell infiltration.

Step 4: Transcriptomics & qRT-PCR

To understand the genetic mechanism.

1. RNA Sequencing (RNA-Seq): RNA was extracted from fungi in the Mycelial Phase (MP), Yeast Phase (YP), and Yeast Phase treated with Azelastine (YPA).
2. Differential Expression Analysis: They compared gene expression between phases and treatments.
3. Validation (qRT-PCR): They used quantitative Real-Time PCR to verify the sequencing results.

   • Mathematical Principle: The relative expression levels of genes were calculated using the $2^{-\Delta\Delta \text{Ct}}$ method.

   • Formula:

     $$\text{Relative Expression} = 2^{-\Delta\Delta \text{Ct}}$$

   • Symbol Explanation:

     • $\text{Ct}$ (Cycle Threshold): The number of cycles required for the fluorescent signal to cross the threshold.

     • $\Delta \text{Ct} = \text{Ct}_{\text{target gene}} - \text{Ct}_{\text{reference gene}}$ (Normalizes the target gene against a housekeeping gene like 18S rDNA).

     • $\Delta\Delta \text{Ct} = \Delta \text{Ct}_{\text{experimental group}} - \Delta \text{Ct}_{\text{control group}}$ (Compares the normalized expression of the experiment against the control).

       ---

5. Experimental Setup

5.1. Datasets & Materials

• Fungal Strains: Sporothrix globosa and Sporothrix schenckii (maintained at Liaoning University).
• Drug Library: FDA-approved small molecule database used for virtual screening.
• Animals: 8-week-old male KM mice (a common outbred mouse stock used in general research).

5.2. Evaluation Metrics

• Binding Energy: Measured in kcal/mol. Represents the strength of the interaction between the drug and the AbaA protein. More negative values indicate stronger binding.

• Optical Density ($\text{OD}_{625}$): Measures the turbidity of the liquid culture.

  $$\text{Absorbance} = -\log_{10} \left( \frac{I}{I_0} \right)$$

  • Where $I$ is the intensity of light passing through the sample and $I_0$ is the initial light intensity. High turbidity (high OD) implies high bacterial/fungal count.

• Granuloma Width: Measured in micrometers ($\mu\text{m}$). A granuloma is a collection of immune cells that forms a "wall" around the infection. Smaller width generally indicates successful treatment or containment.

• Gene Expression (TPM): Transcripts Per Million. A normalization method for RNA-Seq data to allow comparison between samples.

5.3. Baselines

• Negative Control: DMSO (solvent) or untreated medium. Used to ensure the solvent itself isn't toxic.

• Positive Control: Itraconazole. This is the current "gold standard" clinical treatment for Sporothrix. The new drugs (Azelastine/Mefloquine) are compared against this to see if they are as effective.

  ---

6. Results & Analysis

6.1. Virtual Screening Results

From the FDA library, four drugs were initially selected. Based on efficacy, price, and side effects, the authors narrowed it down to two:

• Azelastine

• Mefloquine

  Two other drugs, Avodart and Eltrombopag, were tested but showed no effect (see Figure 2A, B in the paper).

6.2. In Vitro Efficacy (MIC/MBC)

Both drugs showed strong inhibitory activity.

The following are the results from Table 4 of the original paper:

<table> <thead> <tr> <th>Drug</th> <th>MIC (μg/mL)</th> <th>MBC (μg/mL)</th> </tr> </thead> <tbody> <tr> <td>Azelastine - <em>S. globosa</em></td> <td>25</td> <td>50</td> </tr> <tr> <td>Mefloquine - <em>S. globosa</em></td> <td>12.5</td> <td>25</td> </tr> <tr> <td>Azelastine - <em>S. schenckii</em></td> <td>6.25</td> <td>50</td> </tr> <tr> <td>Mefloquine - <em>S. schenckii</em></td> <td>6.25</td> <td>25</td> </tr> </tbody> </table>

Analysis: Mefloquine generally showed lower MIC/MBC values (meaning it is more potent) than Azelastine, particularly against *S.S. globosa.

6.3. In Vivo (Mouse Model) Results

The drugs significantly improved the condition of infected mice.

• Granuloma Reduction: Both drugs reduced the size of granulomas significantly compared to the control group.
• Inflammation:

  • Azelastine: Inflammatory cells were dispersed, and infiltration was milder.

  • Mefloquine: Granulomas were small, but inflammatory infiltration did not show as much improvement as Azelastine relative to the control.

    The following figure (Figure 6 from the original paper) shows the histological staining. Note how the "Control" has massive, dense purple regions (inflammation), while the treated groups look more like the "Mock" (healthy) or Positive Control.

[Image of 6.jpg]

6.4. Transcriptomic Mechanism Analysis

The RNA-Seq analysis provided deep insights into why these drugs work.

• Gene Regulation: Comparing Yeast Phase (YP) vs. Yeast Phase + Azelastine (YPA), 5,256 genes were differentially expressed.
• Downregulated Genes (Virulence): * * EglC: Involved in cell wall integrity and adhesion.
  • **Melanin Genes (e.g.,Melanin Genes (e.g., Scytalone dehydratase): Melanin protects fungi from the host's immune attacks (oxidative stress). Downregulation makes the fungus vulnerable.
• Upregulated Genes (Stress/Repair): * * HtpG (Hsp90 family): A chaperone protein. Its upregulation suggests the fungus is under severe stress and trying to repair damaged proteins.

  • DNA Mismatch Repair Proteins: Indicates DNA damage.

    The following figure (Figure 7 from the original paper) summarizes the GO and KEGG enrichment analysis, highlighting pathways related to metabolism and catalytic activity.

Figure 8 (below) confirms the RNA-Seq data using qRT-PCR. It shows the sharp decrease in virulence genes (like EglC) and the increase in stress genes (like HtpG) in the drug-treated group (Aze) compared to the Yeast Phase (YP).

---

7. Conclusion & Reflections

7..1. Conclusion Summary

This study successfully demonstrates that targeting the abaA gene is a viable strategy for treating Sporotrichosis. Through virtual screening, the authors repurposed Azelastine and Mefloquine as antifungal agents. These drugs inhibit the growth and pathogenic transition of S. globosa and *S. schenS. schenckii both in laboratory cultures and in mouse models. The mechanism involves disrupting cell wall stability and melanin production, while inducing stress responses in the fungus.

7.2. Limitations & Future Work

• Dosing Strategy: The authors noted that while the drugs are FDA-approved, the optimal dosing for antifungal purposes in humans differs from their original uses (allergies/malaria). The current mouse dosages were converted estimates.
• Mechanism Specificity: While docking suggests AbaA is the target, Azelastine is a cationic amphiphilic drug that can cause "phospholipidosis" (lipid accumulation). The authors acknowledge the antifungal effect might be a combination of direct AbaA binding and non-specific membrane effects.
• Future Work: Further exploration of how Mefloquine modulates the host immune system (since it affects granulomas) and optimization of drug scaffolds to create more potent derivatives.

7.3. Personal Insights & Critique

• The Power of Repurposing: This paper is a classic example of how bioinformatics can accelerate drug discovery. Finding that an allergy spray (Azelastine) kills fungi is a non-intuitive discovery that only mass screening could reveal.
• Potential Application: Azelastine is available as a nasal spray. Since Sporotrichosis often starts from spore inhalation or skin contact, a topical or nasal formulation could be a highly practical application derived from this research.
• Validation Rigor: The combination of computational prediction, wet-lab microbiological assays, animal models, and molecular transcriptomics makes the conclusion quite robust. However, proving that the drug physically binds to AbaA (e.g., using Surface Plasmon Resonance or crystallization) would be the ultimate proof to rule out off-target effects.