---
title: "Monitoring Dermatophagoides pteronyssinus culture growth and microbiome dynamics using genetic and"
id: "plos-one-14-genetic-and-microbiomics-approaches-allow-the-monitoring-of-the-growth-of"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-14-genetic-and-microbiomics-approaches-allow-the-monitoring-of-the-growth-of"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359777"
published_at: "2026-10-05T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Monitoring Dermatophagoides pteronyssinus culture growth and microbiome dynamics using genetic and
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-14-genetic-and-microbiomics-approaches-allow-the-monitoring-of-the-growth-of
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0359777)
- **Published At:** 2026-10-05T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study evaluated growth and microbial community dynamics in five independent industrial cultures of the allergenic mite **Dermatophagoides pteronyssinus** used to produce allergen extracts. - Cultures started with equal inoculum and five different batches of culture medium; they were maintained 27–34 days under monitored temperature and humidity and inactivated by freezing prior to harvest. - Mite population density (mites per gram) was assessed by manual counts on sampled culture material at days 1, 15 and final (27–34), and by a designed **qRT-PCR** assay targeting mite DNA (Cq values). - qRT-PCR DNA amplifications were run in technical triplicate at two independent labs; mean Cq correlated inversely with mite density (global Pearson r = -0.69, p = 0.0443). - Two-way ANOVA showed time had a significant effect on mite population (F(2,8)=11.66, p=0.0043), while Cq was influenced by both time and culture type (time F(2,8)=5.716, p=0.0287; culture F(4,8)=5.485, p=0.0200). - Allergen levels (Der p 1 and Der p 2) were measured by ELISA; Cultures 1 and 4 had the highest final Der p 1 levels, while Der p 2 values were more similar among cultures at the end. - Microbiome profiling (16S rRNA and ITS sequencing) was performed on 35 samples including internal mite, external and culture medium (CM) samples; datasets deposited in ENA (PRJEB80324). - Bacterial communities belonged to Firmicutes, Actinobacteria, and Proteobacteria; dominant genera included **Staphylococcus**, **Virgibacillus**, Kocuria, Streptomyces and Cutibacterium. - Fungal communities were dominated by Ascomycota and Basidiomycota, with common genera including **Aspergillus**, **Cryptococcus**, **Malassezia**, and Cladosporium. - Microbiome composition varied between culture batches at the start but tended to become more homogeneous as mite growth progressed; **Staphylococcus**, **Virgibacillus**, and **Malassezia** were implicated as significant taxa in culture progression. - No Gram-negative bacteria or pathogenic bacteria were reported; sequencing removed low-level chloroplast/mitochondrial contamination. Raw sequence counts, ASV tables and methodological details are provided in supplementary files. - The authors propose the qRT-PCR method as an objective tool to monitor mite cultures and to inform harvesting timing for standardized allergen extract production, and they provide a descriptive account of mite–microbe relationships in industrial cultures.
## Clinical Analysis & Structured Key Points
Genetic and microbiomics approaches allow the monitoring of the growth of Dermatophagoides pteronyssinus cultures and their environmental influences | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Dermatophagoides pteronyssinus is cultured in industrial facilities to produce allergen extracts for allergy diagnosis and therapeutic treatment. In these facilities, mite growth and production should be monitored, and exhaustive quality control is mandatory to harvest mites, reach optimal expansion, and avoid potential microbial contamination. In this study, we explored genetic approaches to monitor the growth of five independent D. pteronyssinus cultures. Microbiological studies were performed to characterise the evolution of microbial communities during culture. Finally, we designed a qRT-PCR application to quantify mite populations in the cultures. Our microbiome studies revealed the presence of non-pathogenic bacteria and the absence of Gram-negative bacteria. Despite the variability in microbiome genera at the beginning of the five cultures, the microbiome composition tended to be more homogeneous among the culture batches as mite growth progressed. Specifically, Staphylococcus sp., Virgibacillus sp., and Malassezia sp. appeared to be the most significant taxa involved in culture progression. In summary, we developed a specific method for quantifying and monitoring mite cultures, which could be used to establish an objective method for harvesting mites to manufacture standardised allergen extracts. Additionally, we provide a comprehensive description of the relationship between mites and their symbiotic microorganisms. Citation: Calzada D, Martín-López L, Carnés J (2026) Genetic and microbiomics approaches allow the monitoring of the growth of Dermatophagoides pteronyssinus cultures and their environmental influences. PLoS One 21(10): e0359777. https://doi.org/10.1371/journal.pone.0359777 Editor: Kandasamy Ulaganathan, Osmania University, INDIA Received: September 26, 2025; Accepted: September 17, 2026; Published: October 5, 2026 Copyright: © 2026 Calzada et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The raw data were deposited in the European Nucleotide Archive (accession number: PRJEB80324). Funding: The author(s) received no specific funding for this work. Competing interests: All authors are employees of LETI Pharma S.L. Introduction Dermatophagoides pteronyssinus is the predominant allergenic mite worldwide [ 1 , 2 ]. It produces multiple allergens that cause rhinoconjunctivitis, asthma and contribute to allergic skin diseases in genetically predisposed individuals [ 3 ]. Allergen immunotherapy (AIT) is currently the only aetiopathological treatment for allergic diseases. It involves the repeated administration of increasing amounts of allergen extracts, followed by a maintenance phase, to achieve a form of allergen-specific tolerance that provides clinical benefit for years even after discontinuation [ 4 – 6 ]. Allergen extracts are produced from natural sources, such as pollen or animal epithelia. Mites are mass-cultured in specific facilities of pharmaceutical companies following good manufacturing practices [ 7 , 8 ]. Mite cultures have a typical growth pattern. The initial phase is characterised by a latency period followed by an exponential growth period, resulting in the maximum population of mites. Determining the exact duration of mite production is critical [ 8 , 9 ]; this has led to the determination of the most suitable allergenic composition in the final mite allergen extract. Decisions regarding this are usually made by a qualified workforce that monitors mite growth during culture [ 7 ]. However, having an objective method to quantify the number of individuals could be an important competitive advantage, saving time and improving manufacturing yield. In this sense, quantitative real-time PCR (qRT-PCR) has been demonstrated to be a useful tool to quantify microorganisms in environmental samples [ 10 – 12 ]. Moreover, it has been used to identify different mite species in house dust. However, to our knowledge, this method has never been used to monitor mite production. Mite cultures are produced in a controlled atmosphere to guarantee optimum yield because of their extreme sensitivity to environmental conditions, such as temperature, relative humidity, and diet [ 5 , 8 ]. However, despite efforts to control all these parameters, mite growth presents some variation among batches. Temperatures higher than 30°C and relative humidity higher than 80% can accelerate mite population growth but also favour fungal growth, which may be detrimental to mite survival [ 8 ]. Similarly, Gram-negative bacteria are associated with the production of endotoxins that contaminate mite cultures [ 13 ]. Traditional studies and culture methods have only identified convective microorganisms and do not reflect the entire composition of the mite microbiome [ 14 ]. Therefore, the biological relationship between mites and their symbiotic microorganisms remains poorly understood, and the factors affecting the dynamics of microbial communities have not been completely elucidated [ 15 ]. In recent years, the rapid evolution of next-generation sequencing methods has allowed the study of the microbiome composition in different organisms, including mite species [ 16 – 19 ]. Several studies have analysed the microbiome of house dust mites, while others have analysed microbial community dynamics during mite growth; however, these studies mainly focused on lab cultures and not industrial production. Considering the relevance of mite cultures as raw materials for producing allergen extracts for immunotherapy and allergy diagnosis, we evaluated the growth of five D. pteronyssinus cultures and monitored their microbiomes, including the bacterial and fungal taxa. Therefore, this study focused on exploring qRT-PCR as a tool for monitoring mite growth, determining the microbial communities in different culture medium (diet) batches before the cultures started, analysing the external and internal microbial profiles across mite cultures, and correlating them with mite growth. Materials and methods Fig 1 summarizes the general methodology used in this study. Five independent D. pteronyssinus cultures from five different CM batches were used in this study. Growth evolution and monitoring of internal and external symbiotic microorganisms were performed. Download: PNG larger image TIFF original image Fig 1. General scheme of the study. https://doi.org/10.1371/journal.pone.0359777.g001 Establishment of D. pteronyssinus cultures Five subsequentially D. pteronyssinus cultures (350 g per culture) were maintained at LETI Pharma facilities (Madrid, Spain) for 27–34 days. Each culture starts with 175 grams of an inoculum culture, which consists of D. pteronyssinus stock from LETI Pharma, and 175 grams of the culture media. The culture media used is provided by the same supplier but from five different batches. These media were previously frozen at -20°C for 48 hours. The temperature and humidity were continuously monitored and maintained throughout the culture period. The purity and richness of the mite bodies were analysed before the final harvest. Afterward, the cultures were inactivated by freezing at temperatures below -20°C for 48 h. Quantification of mite growth Quantification of individual mites. Five grams of the culture were sampled at different time points (days 1, 15, and the final day [days 27–34]). Briefly, 10 g of the culture was weighed and resuspended in purified water to quantify the mites. Each sample was independently counted by two trained operators, and values were averaged. The population density was defined as the number of mites per gram of the culture. Quantification via qRT-PCR. DNA isolation: Briefly, 20 µg of the D. pteronyssinus culture or CM was homogenised using a plastic pestle. Two independent biological replicates (DNA extractions) were performed per sample. The homogenate was extracted using a NucleoSpin Plant II kit (Macherey-Nagel, Germany) following the manufacturer’s instructions, with minor modifications. The DNA concentration and quality were determined using a spectrophotometer (ND-1000 Nanodrop; Thermo Fisher Scientific, USA). The DNA was stored at -80°C until further analysis. DNA amplification: DNA amplification was independently performed twice per sample at two different research centers, the R&D Unit of LETI Pharma and a Genetic Unit of Centro de Biología Molecular (CBM) Severo Ochoa (CSIC-UAM) Madrid, Spain. Samples were processed in the same way. Briefly, DNA samples were diluted 1:200 and amplified using a CFX384 Real-Time System C1000 Thermal Cycler (Bio-Rad, USA). Each PCR reaction (10 μl per well) contained TaqMan™ Fast Universal PCR Master Mix, DNA samples, primers (forward: 5′-CATCCAACCAGAGTGGTATTTCC-3′ and reverse: 5′-GCTATTGCGCATACTCCACCTA-3′), and TaqMan TM probe (5 ′-TATGCAATCCTTCGGGCTATCCCATCA-3′) [ 10 ]. The thermocycling conditions were as follows: 95°C for 20 s, and 40 cycles of at 95°C for 3 s and 60°C for 30 s. Each DNA amplification was analyzed in technical triplicate. PCR data were processed using CFX Maestro software to obtain the specific Cq values for each DNA sample. This Cq value refers to cycle threshold. It represents the cycle number at which a sample’s reaction crosses a fluorescence threshold, indicating the detection of the target nucleic acid. Lower Cq values indicate higher target sequence amounts, while higher Cq values suggest lower amounts. The mean Cq value was calculated in triplicate. Major allergen levels Five g of each sample from DPT cultures were extracted (1:10) in PBS 0.01M–NaCl 0.15M for 4 h at 2–8 °C, under continuous magnetic stirring. Then, samples were centrifuged at 7300 g for 30 min, and finally the supernatants were recovered. Then, Der p 1 and Der p 2 levels were quantified by ELISA kits (Inbio Biotechnologies, Charlottesville, VA, USA) following manufacturer’s protocol. Microbiome analysis Six hundred adult mites were isolated and cleaned by washing twice with 96% ethanol. To study the external microbiome, 20 mg of culture or CM alone were obtained. Samples were processed following the same methodology for DNA extraction (as described above). Microbiome analysis was performed on a total of 35 samples corresponding to five culture batches, including internal, external, and culture medium samples collected at defined time points. Bacterial and fungal communities were characterized by sequencing the 16S rRNA gene and the internal transcribed spacer (ITS) region, respectively, using the following primers: V3–V4 Forward: CCTACGGGNGGCWGCAG, V3-V4 Reverse: GACTACHVGGGTATCTAATCC; and ITS primers: ITS86 Forward: GTGAATCATCGAATCTTTGAA, and ITS4 Reverse: TCCTCCGCTTATTGATATGC. Sequencing was performed on a MiSeq platform (Illumina, San Diego, CA, USA) at the Genomics and NGS Core Facility of (CBM-SO). Quality analyses were performed on the reads using FastQC 2 software. Primer sequences were removed, and chimeric sequences were discarded using PANDAseq Assembler and QIIME 2 software. Over 80% of the joined reads were recovered from all the samples. Apart from that chloroplast and mitochondria barcode sequencing contamination in 16S were removed although the number of sequences assigned to them was quite low, with maximum values of 0.6% (mitochondria) and 3.5% (chloroplast). Regarding potential mite ITS contamination, the UNITE database is specifically curated for fungal ITS sequences and does not comprehensively represent metazoan taxa. A detailed bioinformatic workflow is included in the S1 File (Supporting Information). The raw data were deposited in the European Nucleotide Archive (accession number: PRJEB80324). Alignment, phylogenetic tree construction, taxonomic assignment analyses, and functional profiling were performed using two different platforms: https://www.microbiomeanalyst.ca/ and STAMP (Statistical Analysis of Metagenomic Profiles) [ 20 , 21 ]. Statistical analysis The mite population and Cq data were analysed using GraphPad Prism 9 software. Pearson correlations between Cq and mite density (mites/g) were computed per culture and globally; significance threshold p < 0.05. Finally, to evaluate the effects of time and culture conditions on mite population and Cq, two-way ANOVA was performed. For microbiome data, beta diversity was assessed using principal coordinate analysis (PCoA) and hierarchical clustering based on Bray–Curtis distance matrices computed at the ASV (feature) level from data exported from QIIME2. Differences in microbial community composition between groups were evaluated using PERMANOVA, and p-values were calculated using permutation tests (999 permutations), as implemented in MicrobiomeAnalyst. For taxonomic comparisons differences in relative abundances of taxon-levels between sample groups were evaluated using STAMP (v2.1.3) which allows statistical comparison of metagenomic profiles using multiple hypothesis testing and effect size estimation. Comparisons were conducted to identify taxa showing significant differences between samples and culture conditions on relative abundance data at the genus level. Differences between groups were assessed using the G-test (with Yates’ correction) and Fisher’s exact test when appropriate. Multiple testing correction was applied using Bonferroni correction. Taxa with adjusted p < 0.05 were considered statistically significant. Results D. pteronyssinus growth Mite cultures were grown in the absence of contaminants for the entire experimental period. Regarding the mite populations, Fig 2A shows the growth curve of each culture obtained via visual inspection. Culture 1 had a fast and persistent evolution and finished with the highest concentration of mites (181,783 mites/g culture), followed by culture 2 with a final concentration of 146,226 mites/g culture. Culture 5 grew rapidly during the first 15 days and then slowly during the last phase of culture. Cultures 3 and 4 grew slowly and had the lowest mite levels (99,434 and 106,865 mites/g of culture, respectively). Download: PNG larger image TIFF original image Fig 2. Mite growth. (A) Dynamics of the mite population (average mite number ± standard deviation) is shown (B) Mite DNA quantification reported by qRT-PCR in terms of Cq evolution during mite culture (average Cq ± standard deviation). (C) Changes in the mite population and Cq between the final stage and the beginning of the mite cultures. (D) Correlation studies between the mite population and Cq in each culture. (E) Global correlation study. https://doi.org/10.1371/journal.pone.0359777.g002 The two-way ANOVA showed a significant effect of time on mite population (F(2,8) = 11.66, p = 0.0043), accounting for a large proportion of the total variance (57.5%), indicating a consistent increase over time. These results are consistent with the trends observed in Fig 2D - E . In contrast, no significant differences were observed between culture types (F(4,8) = 2.308, p = 0.1460), suggesting that mite population dynamics are primarily driven by temporal factors rather than culture conditions. According to the qRT-PCR results ( Fig 2B ), culture 1 had the lowest Cq value, with a reduction of 1.45 cycles from the beginning, while culture 5 had the highest change Cq value (3.07). Samples from the final step of cultures 3 and 4 also had low values, at 0.90 and 0.95, respectively ( Fig 2C ). Considering this data, we observed that an increase in population doubling was associated with a reduction of more than 1.45 cycles ( Fig 2D ). In this respect, the correlation between the Cq values and mite population observed was significant (r: -0.69; p-value: 0.0443) ( Fig 2E ). To further investigate the relationship between Cq values and experimental factors, a two-way ANOVA was performed using Cq as the dependent variable. The analysis showed a significant effect of both time (two-way ANOVA, F(2,8) = 5.716, p = 0.0287) and culture type (F(4,8) = 5.485, p = 0.0200), indicating that Cq values are influenced not only by temporal changes but also by culture-specific conditions. Regarding allergen levels quantified along the mite cultures, Culture 1 and 4 ended with the highest levels of Der p 1 while Der p 2 levels tended to be more similar at the end of the cultures ( S1 Fig in S1 File ). Microbiome diversity in D. pteronyssinus cultures The raw sequences were denoised, dereplicated, and filtered; the details are included in S1File of Supporting Information. Briefly, the total numbers of non-chimeric sequences used for analysis were 144,712–326,881 for 16S rRNA , and 72,095–68,646 for ITS . The sequences of all samples were grouped to define amplicon sequence variants (ASVs), which were equivalent to 100% operational taxonomic units. We obtained a total number of 2,195 ASVs for 16S rRNA and 917 ASVs for ITS . All ASV-level abundance and taxonomy data are provided in Supplementary S2 Table (16S) and in Supplementary S3 Table (ITS). All the samples included in the study of bacterial communities belonged to three phyla: Firmicutes, Actinobacteria, and Proteobacteria. The heat tree analysis leverages the hierarchical structure of taxonomic classifications. Firmicutes were mainly derived from two large families, Staphylococcaceae and Bacillaceae. Actinobacteria were derived from the families Micrococcales and Propionibacteriaceae, while Proteobacteria were primarily derived from Burkholderiales and Enterobacteriales. The most common genera were Staphylococcus sp., followed by Virgibacillus sp., Kocuria sp ., “Other,” “Not assigned,” Streptomyces sp ., and Cutibacterium sp. ( Fig 3A ). Download: PNG larger image TIFF original image Fig 3. Microbiome diversity in Dermatophagoides pteronyssinus cultures. (A) Heat tree and pie chart of the most common bacterial genera found in all samples, without considering the culture batch or the origin of the samples (external or internal). (B) Fungi analysis: Heat tree and pie chart of the most common six fungal genera found in all samples. The “Not_Assigned” category includes sequences that could not be reliably classified at the genus level, although they are assigned to higher taxonomic ranks. “Other” category groups low-abundance genera that are individually identified but grouped for visualization purposes. https://doi.org/10.1371/journal.pone.0359777.g003 The fungal taxa are presented in Fig 3B , showing two major phyla: Ascomycota and Basidiomycota. Ascomycota were mainly derived into two large families, Saccharomycetes and Eurotiomycetes, while Basidiomycota were derived into genera Malassezia and Cryptococcus . In terms of genera, Aspergillus sp. was the most common, followed by Cryptococcus sp., Malassezia sp. , and Cladosporium sp. To improve the interpretation of the global microbiome structure across samples, we performed unsupervised hierarchical clustering and principal coordinates analysis (PCoA) using all bacterial and fungal taxonomic profiles ( Fig 4 ). Download: PNG larger image TIFF original image Fig 4. Unsupervised hierarchical clustering of all bacterial and fungal DNA samples. (A) Bacterial taxa. (A.1) Heatmap with hierarchical clustering identifying two major sample clusters: cultures 1–2 vs. cultures 3–5, with culture media (CM) forming a distinct branch. (A.2) PCoA grouped by culture batch confirming this separation. (A.3) PCoA grouped by sample type (CM, external, internal). (A.4) PCoA grouped by culture time. (B) Fungal taxa. (B.
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