Proteomic approach to study the effect of pneumocystis jirovecii colonization in idiopathic pulmonary fibrosis

Academic Editor: Lucélia Santi Received: 17 December 2024 Revised: 19 January 2025 Accepted: 24 January 2025 Published: 29 January 2025 Citation: Carmona-Pírez, J.; Salsoso, R.; Charpentier, E.; Olmedo, C.; Medrano, F.J.; Román, L.; de la Horra, C.; de Armas, Y.; Calderón, E.J.; Friaza, V. Proteomic Approach to Study the Effect of Pneumocystis jirovecii Colonization in Idiopathic Pulmonary Fibrosis. J. Fungi 2025,11, 102. https://doi.org/ 10.3390/jof11020102 Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Proteomic Approach to Study the Effect of Pneumocystis jirovecii Colonization in Idiopathic Pulmonary Fibrosis Jonás Carmona-Pírez 1,2,3 , Rocío Salsoso 1, Eléna Charpentier 1,4, Cinta Olmedo 5, Francisco J. Medrano 1,2,6 , Lucas Román 5, Carmen de la Horra 1, Yaxsier de Armas 7,8 , Enrique J. Calderón 1,4,6,* and Vicente Friaza 1,6 1Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla, 41013 Seville, Spain; [email protected] (J.C.-P.); [email protected] (R.S.); [email protected] (F.J.M.); [email protected] (C.d.l.H.); [email protected] (V.F.) 2 Research Network on Chronicity, Primary Care, and Health Promotion (RICAPPS), Institute of Health Carlos III (ISCIII), 28029 Madrid, Spain 3Subdirección Técnica Asesora de Gestión de la Información, Servicio Andaluz de Salud (SAS), 41071 Seville, Spain 4Departamento de Medicina, Hospital Universitario Virgen del Rocío, Facultad de Medicina, Universidad de Sevilla, 41009 Seville, Spain 5Unidad Médico-Quirúrgica de Enfermedades Respiratorias, Hospital Universitario Virgen del Rocío, 41013 Seville, Spain; [email protected] (C.O.); [email protected] (L.R.) 6Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, 28029 Madrid, Spain 7Departments of Clinical Microbiology Diagnostic and Pathology, Hospital Center of Institute of Tropical Medicine “Pedro Kourí”, Havana 11400, Cuba; [email protected] 8 Departamento de Microbiología y Patología, Instituto de Patología Infecciosa y Experimental “Francisco Ruiz Sánchez”, Guadalajara 44100, Mexico *Correspondence: ecalder[email protected]; Tel.: +34-955923096 Abstract: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and interstitial disease with an unclear cause, believed to involve genetic, environmental, and molecular factors. Recent research suggested that Pneumocystis jirovecii (PJ) could contribute to disease exacerbations and severity. This article explores how PJ colonization might influence the pathogenesis of IPF. We performed a proteomic analysis to study the profile of control and IPF patients, with/without PJ. We recruited nine participants from the Virgen del Rocio University Hospital (Seville, Spain). iTRAQ and bioinformatics analyses were performed to identify differentially expressed proteins (DEPs), including a functional analysis of DEPs and of the protein–protein interaction networks built using the STRING database. We identified a total of 92 DEPs highlighting the protein vimentin when comparing groups. Functional differences were observed, with the glycolysis pathway highlighted in PJcolonized IPF patients; as well as the pentose phosphate pathway and miR-133A in noncolonized IPF patients. We found 11 protein complexes, notably the JAK-STAT signaling complex in non-colonized IPF patients. To our knowledge, this is the first study that analyzed PJ colonization’s effect on IPF patients. However, further research is needed, especially on the complex interactions with the AKT/GSK-3 β /snail pathway that could explain some of our results. Keywords: idiopathic pulmonary fibrosis; proteomics; Pneumocystis jirovecii;Pneumocystis colonization; iTRAQ quantification; protein–protein interaction networks 1. Introduction Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and eventually fatal interstitial lung disease (ILD) characterized by the formation of excessive fibrous tissue J. Fungi 2025,11, 102 https://doi.org/10.3390/jof11020102 J. Fungi 2025,11, 102 2 of 16 in the lungs, that occurs principally in older adults. This scarring leads to a progressive decline in pulmonary function, typically presenting with symptoms such as exertional dyspnea and a persistent dry cough [ 1 ]. The term “idiopathic” highlights the absence of a known cause for the disease, differentiating it from other forms of pulmonary fibrosis associated with known etiologies such as connective tissue diseases or environmental exposures [ 2 ]. Pathologically, IPF is associated with a distinct histological pattern known as usual interstitial pneumonia (UIP), which is distinguished from other ILD patterns by its temporal and spatial heterogeneity. While IPF is often discussed as a singular condition, it is important to acknowledge variations within its clinical spectrum, potentially related to differences in genetic predispositions or environmental factors [3]. The precise cause of IPF remains elusive, but its pathogenesis is thought to involve a complex interplay of genetic, environmental, and molecular factors. It is widely accepted that aberrant wound healing and persistent alveolar epithelial injury play a central role. Repeated micro-injuries to the alveolar epithelium lead to an abnormal repair process, characterized by the excessive activation of fibroblasts and myofibroblasts, ultimately resulting in the deposition of extracellular matrix (ECM) components, particularly collagen. Over time, this excessive ECM deposition compromises lung architecture and function. Genetic mutations, such as those in genes involved in telomere maintenance, have been implicated in familial cases of IPF, while environmental exposures, such as cigarette smoke, air pollution, and certain occupational hazards, may contribute to disease onset. Epigenetic modifications, oxidative stress, and dysregulated immune responses are also thought to play roles in the fibrotic process [2,4]. The potential involvement of infectious agents in the pathogenesis of IPF has been the subject of increasing interest. Several viral infections, particularly those caused by herpesviruses, including Epstein–Barr virus (EBV) and cytomegalovirus (CMV), have been investigated as potential triggers for the initiation or exacerbation of pulmonary fibrosis. These viruses can cause chronic inflammation and alveolar epithelial cell damage, which may contribute to the repetitive injury that is characteristic of IPF [ 5 ]. In addition, bacterial and fungal pathogens may exacerbate lung injury in susceptible individuals by inducing immune dysregulation [ 6 ]. However, whether infectious agents serve as primary causative factors or merely act as cofactors in disease progression remains an open question. Recent studies suggest that, while infectious agents may not directly cause IPF, they could play a significant role in disease exacerbations and influence disease severity through modulation of the host immune response. The presence of Pneumocystis jirovecii (PJ) colonization has been described in chronic lung diseases, including idiopathic pulmonary fibrosis [ 7 , 8 ]. Several studies have started to explore the molecular and immune responses triggered by PJ colonization in the context of chronic pulmonary diseases. These investigations indicate a complex interplay between the fungus and the host’s immune system, suggesting that various immune dysregulations might exacerbate lung injury and promote fibrosis [ 9 – 11 ]. This article explores the potential influence of PJ colonization on the pathogenesis of IPF, highlighting both their direct and indirect effects on lung health. We performed a proteomic analysis to identify differences in the protein profile of IPF and control patients, with and without PJ colonization. 2. Materials and Methods 2.1. Participants Participants were recruited from the Respiratory Endoscopy Unit at the Virgen del Rocio University Hospital (Seville, Spain). All participants completed written informed consent and, according to our hospital’s regulations, the procedure for requesting and authorizing research studies was completed. The diagnosis of IPF was made by lung biopsy J. Fungi 2025,11, 102 3 of 16 in all cases. The control group was patients with mild hemoptysis without malignancy. Participants were also divided into colonized or non-colonized PJ groups based on the results of PCR analysis. The exclusion criteria were underlying autoimmune disorders, malignant disease, immunosuppressive therapy (cytotoxic and steroids treatment), positive status for human immunodeficiency virus, and any clinical symptom and/or radiological evidence that could correspond to pneumonia. Bronchoalveolar lavage (BAL) samples were collected from selected patients for further analysis. The characteristics of the study population are described in Table 1. Table 1. Characteristics of the study population. Sample Group Label Sex Age 1 Control with PJ 114 Female 48 2 Control without PJ 115 Female 61 3 IPF with PJ 116 Male 60 4 IPF with PJ 116 Male 64 5 IPF with PJ 116 Male 76 6 IPF with PJ 116 Male 66 7 IPF with PJ 116 Male 67 8 IPF without PJ 117 Female 69 9 IPF without PJ 117 Male 45 PJ, Pneumocystis jirovecii; IPF, idiopathic pulmonary fibrosis. 2.2. Sample Collection Bronchoalveolar lavages were conducted following standard procedures. Briefly, sterile saline solution (0.9% w/v) was instilled under local anesthesia, with a fiberoptic bronchoscope (Type 40; Olympus, Tokyo, Japan) and recovered immediately by suction. About 4 mL of each BAL sample were transported immediately to the laboratory in a sterile refrigerated container where they were aliquoted in two aliquots of 250 µ L for Pneumocystis detection and the rest of the sample was used for the proteomic study. 2.3. Detection of Pneumocystis jirovecii Whole BAL aliquots were digested with proteinase K at 56 ◦ C with 10 mM of EDTA and 0.5% of SDS over 4 h and total DNA was extracted and purified using Nucleospin Tissue Kit (Macherey-Nagel, Duren, Germany). The presence of Pneumocystis was analyzed by nested PCR amplification of the Pneumocystis mtLSU rRNA gene as described elsewhere [ 12 ]. Patients whose PJ DNA was detected by nested PCR in both BAL aliquots and in two independent analyses were considered positive by PJ. While patients whose samples, following the same criteria, were always negative were considered non-colonized patients. To prevent false positives from contamination, pipettes with filters were used in all stages. DNA extraction, preparation of the reaction mixture, PCR amplification, and detection were performed in different areas. To detect any cross-contamination, all PCR was performed with a negative control of sterile water. Each experiment was performed at least twice. 2.4. iTRAQ Analysis An untargeted proteomic analysis with 4-plex isobaric tags for relative and absolute quantification (iTRAQ) was performed to identify differences in the protein profile of IPF and control patients with and without PJ. Analysis was performed in five phases: extraction and protein depletion, trypsin digestion and iTRAQ labeling, high pH reversed-phase fractionation, liquid chromatography–mass spectrometry nanoLC nanoESI-QTOF (QSTAR ® XL Hybrid LC/MS/MS System, Applied Biosystems) and MS data analysis using Protein Pilot software 5.0.2 (AB Sciex). J. Fungi 2025,11, 102 4 of 16 Briefly, BAL fluid samples were centrifuged at 800 × gfor 10 min, and the supernatants were collected. Sample protein concentration was measured by Braford assay and a similar quantity of proteins was added from each sample to produce the different pools. Samples from patients with IPF, with and without PJ, were pooled separately. The supernatants were concentrated with Amicon ® Ultra 0.5 mL 3 k Ultracell (Merk Millipore, Milan, Italy). Subsequently, six volumes of cold acetone were added to the concentrated supernatants and incubated for 4 h at − 20 ◦ C. The mixture was centrifuged at 14,000 × gfor 10 min, and the resulting pellets were resuspended in a protein solubilization buffer. For further sample preparation, albumin and IgG were depleted using the ProteoPrep ® Immunoaffinity Albumin and IgG Depletion Kit (Sigma-Aldrich). The resulting proteins were precipitated using a methanol–chloroform method and resuspended in 1% SDS at a final concentration of 5 mg/mL. Subsequently, 100 µ g of peptides from each group were reduced, cysteine-blocked, and digested with trypsin. The digested peptides were labeled with iTRAQ reporters 114, 115, 116, and 117, according to the manufacturer’s instructions (Applied Biosystems). The reference was assigned to a patient with mild hemoptysis without Pneumocystis labeled as 114-iTRAQ. The contents of all iTRAQ reagent-labeled sample tubes were combined into one tube to perform the LC-MS/MS analysis. We performed a comparative analysis to identify differentially expressed proteins (DEPs) by applying a Student’s ttest to detect expression changes, considering downregulation and upregulation a fold change of 0.8 and 1.2, respectively, and p-value ≤ 0.05. All data were analyzed using Protein Pilot software (AB Sciex) and MASCOT v2.1 software (Matrix Science) and were searched against the NCBInr database. 2.5. Pathway and Process Enrichment Analysis We performed a pathway and process enrichment analysis for each given gene list, in the following ontology sources: GO Biological Processes, Reactome Gene Sets, KEGG pathways, WikiPathways, and MIRNA. We used the R package gProfiler2 [ 13 ] and the g:Profiler—interoperable web service [ 14 ] to perform this analysis. Only genes with at least one annotation in the genome were used as the enrichment background. We applied the g:SCS method [ 14 ] for computing multiple testing corrections for p-values gained from GO and pathway enrichment analysis. The experiment-wide threshold was a = 0.05 and the user threshold p-value = 0.05. The g:SCS method also pre-calculates a threshold t for list sizes up to 1000 genes. It corresponds to the 5% upper quantile of randomly generated queries of that fixed input size. The resulting p-values are corrected by multiplying them by the initial experiment-wide threshold a = 0.05 and the ratio of the threshold t. 2.6. Protein–Protein Interaction Networks We built a protein–protein interaction network for each gene list using the STRING database [ 15 ]. Only physical interactions (physical score > 0.150) were included. Genetic interactions were not included because they capture functional relationships that could be like those in Gene Ontology and we aim to detect protein complexes. The resultant three networks were analyzed with the Cytoscape StringApp v3.7.2 [ 16 ] and the clusterMaker Cytoscape plugin [ 17 ]. The Molecular Complex Detection (MCODE) algorithm [ 18 ] was applied to identify densely connected network clusters. The algorithm has three steps: (1) Weighting: It is proportional to the interconnectivity between nodes; those more interconnected nodes will have a higher score; (2) Complex prediction: It orders the nodes based on the weighting, starting with the node with the highest weight and iteratively moving out. Then, it adds nodes to the complexes considering a given threshold; J. Fungi 2025,11, 102 5 of 16 (3) Filtering: Hair and fluff filters are applied to improve the quality of the clusters. The MCODE method was specifically designed to find protein complexes in PPI networks [ 18 ], and we set its parameters to optimize results accuracy (depth = 100; node score% = 0; haircut = true; fluff = false; percentage for complex fluffing = 0.2) [ 19 ]. Once we obtained the protein complexes, we performed a pathway and process enrichment analysis as described previously. The complexes obtained were then named taking into consideration the best-scoring terms and the consensus of all researchers. All the bioinformatics analyses were conducted using RStudio software (R version 4.3.3, RStudio, Boston, MA, USA), Cytoscape software (version 3.10.2, Institute for Systems Biology, Seattle, WA, USA), and interoperable web resources g:Profiler [ 14 ] and STRING [15]. 3. Results 3.1. iTRAQ-Based Proteomics Analysis to Identify DEPs We identified a total of 92 proteins differentially expressed among control with PJ, IPF with PJ, and IPF without PJ groups (please see Table 2and Table S1 in the Supplementary Materials file). Among them, 71 DEPs were associated with the control group with PJ, including 38 downregulated and 33 upregulated proteins (Figure 1A and Table 2). We identified 68 DEPs in the IPF with PJ group of which 33 were downregulated and 35 upregulated (Figure 1B and Table 2). Regarding the IPF without PJ group, we identified 61 DEPs, 27 downregulated, and 34 upregulated (Figure 1C and Table 2). Table 2. Summary of DEPs. Number of Proteins a Group Downregulated (<0.8) Upregulated (>1.2) Total Control with PJ 38 33 71 IPF with PJ 33 35 68 IPF without PJ 27 34 61 Global 92 a Fold change is the iTRAQ quantitative result. Fold change of <0.8 represents downregulation and fold change of >1.2 represents upregulation; PJ, Pneumocystis jirovecii; IPF, idiopathic pulmonary fibrosis. We detected that only one protein (immunoglobulin J chain) was downregulated in the IPF patients with PJ and upregulated in IPF patients without PJ. With no proteins regulated in the opposite direction between these two groups (Figure 1D and Table S1). Regarding the comparison between the IPF group with PJ and the control group with PJ, we found that two proteins (hemoglobin subunits alpha and beta) were downregulated in the first group and upregulated in the second one, while tyrosine 3-monooxygenase and vimentin were regulated in opposite directions (Figure 1E and Table S1). Similarly, this occurred in the comparison between IPF patients without PJ and controls with PJ, in which we identified hemoglobin subunits alpha and beta, and keratin 1 as downregulated in the IPF group and upregulated in the control group with PJ, while the proteins phosphoglycerate kinase 1, alpha-1-antitrypsin, pulmonary surfactant-associated protein B and vimentin were regulated in antagonistic directions (Figure 1F and Table S1). To summarize these results, Table 3shows DEPs with opposite changes with significant p-values in the three groups, showing that only three proteins fulfilled these requirements: vimentin and hemoglobin subunits alpha and beta. J. Fungi 2025,11, 102 6 of 16 J. Fungi 2025, 11, x FOR PEER REVIEW 6 of 17 Figure 1. Differentially expressed proteins (DEPs) associated with the groups: control with Pneumocys jirovecii (PJ), idiopathic pulmonary fibrosis (IPF) colonized by PJ, and IPF non-colonized by PJ (A–C). The DEPs were identified by comparing to the control group non-colonized by PJ; the comparison results are presented as volcano plots. The protein expression differences with a fold change of 0.8 or 1.2 (cutoff line, x = 0.260344) were considered significant at a p value of 0.05 (cutoff line, y = Figure 1. Differentially expressed proteins (DEPs) associated with the groups: control with Pneumocys jirovecii (PJ), idiopathic pulmonary fibrosis (IPF) colonized by PJ, and IPF non-colonized by PJ (A–C). The DEPs were identified by comparing to the control group non-colonized by PJ; the comparison results are presented as volcano plots. The protein expression differences with a fold change of 0.8 or 1.2 (cutoff line, x = 0.260344) were considered significant at a pvalue of 0.05 (cutoff line, y = 1.30). The numbers in the colored frames indicate the identified DEP numbers. The dots that are labeled with protein designations represent some DEPs in the study. (D–F) Overlapping DEPs were analyzed, and the shared protein numbers between the groups are shown as a Venn diagram. (G) Numbers of shared proteins among the groups are shown as a Venn diagram. J. Fungi 2025,11, 102 7 of 16 Table 3. DEPs with opposite changes in control patients with PJ vs. IPF (with/without PJ) groups with significant p-values (p≤0.05) in the three groups. Fold Change a Protein Name Gene Symbol Control with PJ IPF with PJ IPF Without PJ Vimentin VIM 0.563 3.283 2.912 Hemoglobin subunit alpha HBA1 6.184 0.559 0.729 Hemoglobin subunit beta HBB 4.857 0.557 0.494 a Fold change is the iTRAQ quantitative result. Fold change of <0.8 represents downregulation and fold change of >1.2 represents upregulation; PJ, Pneumocystis jirovecii; IPF, idiopathic pulmonary fibrosis. As our objective was to explore the mechanisms of PJ in patients with and without IPF, our next step was the functional analysis of the three groups under study. 3.2. Pathway and Process Enrichment Analysis The functions of DEPs in the control group with PJ, the IPF group with PJ, and the IPF group without PJ were explored using GO Biological Processes, Reactome Gene Sets, KEGG pathways, WikiPathways, and microRNA terms (Figure 2). J. Fungi 2025, 11, x FOR PEER REVIEW 8 of 17 Figure 2. Pathway and process enrichment analysis associated with the groups: control with Pneumocystis jirovecii (PJ), idiopathic pulmonary fibrosis (IPF) colonized by PJ, and IPF non-colonized by PJ (A–C). We performed a functional analysis for each given gene list, in the following ontology sources: GO Biological Processes, Reactome Gene Sets (REAC), KEGG pathways, WikiPathways (WP) and MIRNA. It includes up to 5 terms in each category and ordered by −log10 p-value. We built a protein-protein interaction network for each gene list (respectively (D,E,F)) using the STRING database. We applied the Molecular Complex Detection (MCODE) algorithm to identify densely connected protein complexes. Then, we performed a functional analysis as described previously. The complexes found were named considering the best-scoring terms and the consensus of all researchers. In the control group with PJ, we obtained 108 enriched GO terms for Biological Processes, 22 Reactome terms, 3 KEGG pathways, 6 WikiPathways, and 1 microRNA term (Table S2). The top terms for each category are shown in Figure 2A, highlighting the GO Biological Processes terms humoral immune response (GO:0006959), response to other organism (GO:0051707), and hydrogen peroxide catabolic process (GO:0042744); the Reactome Gene Set terms defined as neutrophil degranulation (R-HSA-6798695), innate immune system (R-HAS-168249), metal sequestration by antimicrobial proteins (R-HSA6799990), binding and uptake of ligands by scavenger receptors (R-HSA-2173782), and surfactant metabolism (R-HSA-5683826); the KEGG pathway terms neutrophil extracellular trap formation (KEGG:04613) and complement and coagulation cascades (KEGG:04610); the WikiPathways selenium micronutrient network (WP:WP15), prostaglandin synthesis and regulation (WP:WP98), folate metabolism (WP176), and vitamin B12 metabolism (WP1533); and the microRNA term hsa-miR-133a-3p Figure 2. Pathway and process enrichment analysis associated with the groups: control with Pneumocystis jirovecii (PJ), idiopathic pulmonary fibrosis (IPF) colonized by PJ, and IPF non-colonized by PJ (A–C). We performed a functional analysis for each given gene list, in the following ontology sources: GO Biological Processes, Reactome Gene Sets (REAC), KEGG pathways, WikiPathways (WP) and MIRNA. It includes up to 5 terms in each category and ordered by − log10 p-value. We built a protein-protein interaction network for each gene list (respectively, (D,E,F)) using the STRING database. We applied the Molecular Complex Detection (MCODE) algorithm to identify densely connected protein complexes. Then, we performed a functional analysis as described previously. The complexes found were named considering the best-scoring terms and the consensus of all researchers. J. Fungi 2025,11, 102 8 of 16 In the control group with PJ, we obtained 108 enriched GO terms for Biological Processes, 22 Reactome terms, 3 KEGG pathways, 6 WikiPathways, and 1 microRNA term (Table S2). The top terms for each category are shown in Figure 2A, highlighting the GO Biological Processes terms humoral immune response (GO:0006959), response to other organism (GO:0051707), and hydrogen peroxide catabolic process (GO:0042744); the Reactome Gene Set terms defined as neutrophil degranulation (R-HSA-6798695), innate immune system (R-HAS-168249), metal sequestration by antimicrobial proteins (R-HSA6799990), binding and uptake of ligands by scavenger receptors (R-HSA-2173782), and surfactant metabolism (R-HSA-5683826); the KEGG pathway terms neutrophil extracellular trap formation (KEGG:04613) and complement and coagulation cascades (KEGG:04610); the WikiPathways selenium micronutrient network (WP:WP15), prostaglandin synthesis and regulation (WP:WP98), folate metabolism (WP176), and vitamin B12 metabolism (WP1533); and the microRNA term hsa-miR-133a-3p Regarding the IPF group with PJ, we detected 82 enriched GO terms for Biological Processes, 17 Reactome terms, 1 KEGG pathway, and 5 WikiPathways terms (Table S3). No microRNA term was identified. The top terms are shown in Figure 2B. GO Biological Processes terms obtained were very similar to those identified in the other groups, highlighting humoral immune response (GO:0006959), and biological process involved in interspecies interaction between organisms (GO:0044419). Regarding the Reactome Gene Set terms, we detected some differences compared to the control group with PJ and similarities to the IPF group without, highlighting the terms gene and protein expression by JAK-STAT signaling after interleukin-12 stimulation (R-HSA-8950505) and interleukin-12 signaling (R-HSA-9020591). We identified only one KEGG term, glycolysis/gluconeogenesis (KEGG:00010). Finally, the WikiPathways found were selenium micronutrient network (WP15), prostaglandin synthesis and regulation (WP98), folate metabolism (WP176), vitamin B12 metabolism (WP1533), highlighting the term NRF2 pathway (WP2884). In the IPF group without PJ, we identified 55 enriched GO terms for Biological Processes, 16 for Reactome Gene Sets, 3 for KEGG pathways, 3 for WikiPathways terms, and 1 for microRNA term (Table S4). The top terms for each category are shown in Figure 2C; the results for GO Biological Processes were like those found in the other groups, standing out the terms humoral immune response (GO:0006959) and response to other organisms (GO:0051707). Regarding the Reactome Gene Set terms, the results were like those obtained in the IPF group with PJ, highlighting the terms gene and protein expression by JAK-STAT signaling after interleu-kin-12 stimulation (R-HSA-8950505) and interleukin-12 signaling (R-HSA-9020591). Among the KEGG pathways, the terms neutrophil extracellular trap formation (KEGG:04613) and pentose phosphate pathway (KEGG:00030) were highlighted. The significant WikiPathway terms were selenium micronutrient network (WP15), NRF2 pathway (WP2884), and vitamin B12 metabolism (WP1533), like those obtained in the IPF group. Finally, we also identified the microRNA term hsa-miR-133a-3p. 3.3. Protein–Protein Interaction (PPI) Networks: Cluster and Enrichment Analysis The PPI network from the control group with PJ consisted of 62 nodes and 185 edges (Figure S1), and we identified four protein communities (Figure 2D), which we referred to as metal sequestration/neutrophil aggregation, scavenger receptors/acute inflammatory response, neutrophil degranulation, and amyloid fiber formation/senescence complexes. The metal sequestration/neutrophil aggregation complex was characterized by metal sequestration by antimicrobial proteins (R-HSA-6799990), neutrophil aggregation (GO:0070488), sequestering of zinc ion (GO:0032119), prostaglandin synthesis and regulation (WP98) and vitamin D receptor pathway (WP2877). It included the proteins J. Fungi 2025,11, 102 9 of 16 ACTB, ANXA2, ANXA5, LMNA, LTF, PGK1, S100A4, S100A6, S100A8, S100A9 and VIM. The scavenger receptors/vesicle-mediated transport complex was characterized by the terms scavenging of heme from plasma (R-HSA-2168880), acute inflammatory response (GO:0002526), binding and uptake of ligands by scavenger receptors (R-HSA-2173782) and post-translational protein phosphorylation (R-HSA-8957275); it included the proteins A2M, APOA1, C3, HBB, HP, HPX, SERPINA1 and TF. The neutrophil degranulation complex was defined by the terms antimicrobial peptides (R-HAS-6803157), neutrophil degranulation (RHSA-6798695), response to yeast (GO:0001878), and transcriptional misregulation in cancer (KEGG:05202); it included the proteins ELANE, H3-5, MPO, LYZ, and PRTN3. Finally, the amyloid fiber formation/senescence complex was defined by the terms amyloid fiber formation (R_HSA-977225), senescence-associated secretory phenotype (R_HSA_2559582), and oxidative stress-induced senescence (R-HAS-2559582), and it was formed by the proteins H2AC7, H4C1, and UBC. The PPI network from the IPF group with PJ consisted of 53 nodes and 133 edges (Figure S2), and we identified 3 protein communities (Figure 2E) which we referred to as metal sequestration/defense response to fungus, humoral immune response/defense response to symbiont, and amyloid fiber formation/senescence complexes. The metal sequestration/defense response to fungus complex was characterized by the terms metal sequestration by antimicrobial proteins (R-HSA-6799990), defense response to fungus (GO:0050832), chemotaxis (GO:0006935), and taxis (GO:0042330); it was formed by the proteins ANXA1, ANXA2, APOA1, CALR, LTF, MPO, S100A4, S100A8, and S100A9. The humoral immune response/defense response to symbiont complex was defined by the terms humoral immune response (GO:0006959), defense response to symbiont (GO:0140546), and neutrophil degranulation (R-HAS-6798695); it was composed by the proteins A2M, C3, ELANE, HBB, HPX, H3-5, LYZ, PRTN3, and TF. Finally, the amyloid fiber formation/senescence complex was the same as previously described in the PPI network from the control group with PJ. Finally, the PPI network from the IPF group without PJ was integrated by 50 nodes and 117 edges (Figure S3), and we detected 4 protein communities (Figure 2F), which we named JAK-STAT signaling/interleukin 12, scavenger receptors/vesicle-mediated transport, humoral immune response, and amyloid fiber formation/senescence complexes. The JAK-STAT signaling/interleukin 12 complex was characterized by the terms gene and protein expression by JAK-STAT signaling after IL 12 stimulation (R-HSA-8950505), IL12 signaling (R-HAS-9020591), and membrane-to-membrane docking (GO:0022614), among others; being formed by the proteins EZR, HNRNPA2B1, LCP1, LMNA, MSN, PGK1, and VIM. The scavenger receptors/vesicle-mediated transport protein complex was like that identified in the control group with PJ and defined by the terms scavenging of heme from plasma (R-HSA-114608), vesicle-mediated transport (R-HSA-5653656) and binding and uptake of ligands by scavenger receptors (R-HSA-2173782); it included the proteins APOA1, HBB, HP, HPX, SERPINA1 and TF. The humoral immune response complex was characterized by the terms antimicrobial humoral response (GO:0019730), antimicrobial peptides (R-HSA-6803157), humoral immune response (GO:0006959) and systemic lupus erythematosus (KEGG:05322), being similar to that obtained in the IPF group with PJ; it was formed by the proteins ELANE, H3-5, LTF and PRTN3. Finally, the amyloid fiber formation/senescence complex was the same as previously described. 4. Discussion This study explored the protein expression profiles of IPF and control patients and the effect of PJ colonization on them. We applied an iTRAQ-based method to identify differences in protein profiles and detected relevant DEPs, highlighting the protein vimentin. J. Fungi 2025,11, 102 16 of 16 48. Wang, C.; Guo, K.; Gao, D.; Kang, X.; Jiang, K.; Li, Y.; Sun, L.; Zhang, S.; Sun, C.; Liu, X.; et al. 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