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24 October 2023

Development of a Taxon-Specific Real-Time Polymerase Chain Reaction Method to Detect Trichoderma reesei Contaminations in Fermentation Products

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Sciensano, Transversal Activities in Applied Genomics (TAG), Rue Juliette Wytsman 14, 1050 Brussels, Belgium
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Author to whom correspondence should be addressed.
Fermentation2023, 9(11), 926;https://doi.org/10.3390/fermentation9110926
This article belongs to the Section Fermentation Process Design
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Abstract

Recently, a genetically modified microorganism (GMM) detection strategy using real-time PCR technology was developed to control fermentation products commercialized in the food and feed chain, allowing several unexpected GMM contaminations to be highlighted. Currently, only bacterial strains are targeted by this strategy. Given that fungal strains, like Trichoderma reesei, are also frequently used by the food industry to produce fermentation products, a novel real-time PCR method specific to this fungal species was developed and validated in this study to reinforce the GMM detection strategy. Designed to cover a sequence of 130 bp from the translation elongation factor alpha 1 (Tef1) gene of T. reesei, this real-time PCR method, namely TR, allows for the screening of commercial fermentation products contaminated with T. reesei, genetically modified or not, which is one of the major fungal species used as an industrial platform for the manufacturing of fermentation products. The developed real-time PCR TR method was assessed as specific and sensitive (LOD95% = eight copies). In addition, the developed real-time PCR TR method performance was confirmed to be in line with the “Minimum Performance Requirements for Analytical Methods of GMO Testing” of the European Network of GMO Laboratories. The validated real-time PCR TR method was also demonstrated to be applicable to commercial microbial fermentation products. Based on all these results, the novel real-time PCR TR method was assessed as valuable for strengthening the current GMM detection strategy regarding major fungal species used by the food industry to produce microbial fermentation products.

1. Introduction

Both bacterial and fungal strains, genetically modified or not, are broadly used by the food industry for the production of fermentation products, including enzymes and additives. Among these microbial species of interest, Bacillus subtilis and B. licheniformis for bacterial species, and Trichoderma reesei, Aspergillus niger, and A. oryzae for fungal species, are the majority used [1,2,3,4,5,6,7,8,9,10,11,12].
Recently, a real-time PCR strategy was developed to detect genetically modified bacterial strains, and numerous commercial fermentation products were unexpectedly notified for genetically modified bacterial contamination, including DNA and viable cells [13,14,15,16,17,18,19,20,21,22]. In addition to the subsequent associated traceability concerns, potential public health concerns were raised. Indeed, since genetically modified microorganisms (GMMs) used for the production of fermentation products generally carry antimicrobial resistance genes as selection markers, there were also inquiries about the potential horizontal transfer of such antimicrobial resistance genes to gut microbiota and pathogens [23,24,25,26,27,28,29,30,31,32,33].
Currently, the developed GMM detection strategy focuses exclusively on bacterial strains. However, given that approximately half of fermentation products are made using fungal strains [2,3], the proposed GMM detection strategy needs to be reinforced regarding fungal contamination. For this purpose, a novel real-time PCR method was developed in this study to screen for the presence of T. reesei, one of the major fungal species used as an industrial platform for manufacturing fermentation products [2,3,34,35]. This developed taxon-specific real-time PCR method, namely TR, was assessed for its performance, including its specificity, sensitivity, and applicability. The real-time PCR TR method was also evaluated for its compatibility with the “Minimum Performance Requirements (MPR) for Analytical Methods of GMO Testing” of the European Network of GMO Laboratories (ENGL) to assess its suitability for enforcement purposes [36].

2. Materials and Methods

2.1. Materials

DNA from an artificially synthetized control plasmid (Genecust) carrying a single copy of the T. reesei sequence targeted by the real-time PCR TR method was used. DNA from Homo sapiens (G3041 from Promega), Zea mays (ERM-BF413ak from JRC IRMM), wild-type (WT) microbial species, and genetically modified bacterial strains (B. subtilis RASFF2014.1249 and B. velezensis RASFF2019.333) was obtained as previously reported (Table 1, Table 2 and Table 3). All WT microbial species were collected from the BCCM (Belgian Coordinated Collection of Microorganisms) Consortium (collection number starting by IHEM, MUCL, and LMG), the American Type Culture Collection (ATCC), the German Collection of Microorganisms and Cell Cultures GmbH (DSMZ), the CBS-KNAW Fungal Biodiversity Centre (collection number starting with CGS), and Sciensano (collection number starting with TIAC and RASFF). The associated strain collection numbers are indicated in Table 2. DNA from 10 microbial fermentation products (samples n°1–10) commercialized on the European market was extracted using the NucleoSpin Food kit (Macherey-Nagel), as previously reported (Table 4). DNA concentration and purity were measured and evaluated as previously described [15,16,17,18,19,20,21,22].
Table 1. Targeted T. reesei sequence and oligonucleotides from the newly developed real-time PCR TR method targeting T. reesei.
Table 2. Specificity evaluation of the newly developed real-time PCR TR method.
Table 3. Sensitivity evaluation of the newly developed real-time PCR TR method.
Table 4. Applicability evaluation of the newly developed and in-house validated real-time PCR TR method using commercial food enzyme products.

2.2. Development and Validation of the Real-Time PCR TR Method

Based on previous studies [37,38,39], the translation elongation factor alpha 1 (Tef1) gene was selected to develop a taxon-specific real-time PCR method targeting T. reesei species. Using the Primer3 (v. 0.4.0) software, a set of primers and probes was designed, allowing for the amplification of 130 bp of the T. reesei Tef1 gene (Table 1) [40,41]. Each real-time PCR assay was applied as previously described. The real-time PCR program comprised an annealing/extension step at 60 °C. Each real-time PCR run included a no-template control (NTC) and a positive control (DNA from the T. reesei IHEM 5264 strain) (Table 2).

2.2.1. Specificity Evaluation

First, the in silico specificity of the newly developed real-time PCR TR method was tested. On the one hand, the sequence amplified by the real-time PCR TR method targeting T. reesei (Table 1) was blasted against the NCBI nucleotide collection (nr/nt) database (access on June 2022; default parameters) as well as against the NCBI RefSeq Genome database (access on June 2022; default parameters; Fungi (taxid:4751)) (Tables S1 and S2). On the other hand, the hybridization properties of the targeted regions and the designed set of primers and probes were examined using SCREENED v1.0 [22,42]. The used parameter settings were (i) maximum 10% for mismatches in the annealing sites, (ii) minimum 90% for the length of the alignment in the annealing sites, and (iii) no mismatch in the last five nucleotides at the 3′ end for primers. The targeted regions were collected from a sequence dataset of the NCBI Genome database (access on June 2022; filter: Trichoderma). The database contained 90 items, including T. afroharzianum, T. arundinaceum, T. asperelloides, T. asperellum, T. atrobrunneum, T. atroviride, T. brevicompactum, T. brevicrassum, T. citrinoviride, T. cornu-damae, T. erinaceum, T. gamsii, T. gracile, T. guizhouense, T. hamatum, T. harzianum, T. koningii, T. koningiopsis, T. lentiforme, T. lixii, T. longibrachiatum, T. oligosporum, T. parareesei, T. pleuroti, T. pseudokoningii, T. reesei, T. semiorbis, T. simmonsii, T. virens, and T. viride.
The specificity of the developed real-time PCR TR method was then experimentally tested in triplicates on 10 ng of DNA from positive and negative materials (Table 2). For the positive materials, DNA extracted from 5 WT T. reesei strains was used. For the negative materials, DNA extracted from animals (Homo sapiens), plants (Zea mays), 113 WT microbial strains, bacterial and fungal species often used by the food industry to manufacture fermentation products, and 2 genetically modified Bacillus strains producing vitamin B2 or protease (RASFF2014.1249 and RASFF2019.3332) was used [15,16,17,18,19,20,21,22].
The amplicon generated from the T. reesei IHEM 5264 strain using the developed real-time PCR TR method was purified and sequenced as previously described [20]. Using the Clustal Omega multiple sequence alignment software (v1.2.4) with default parameters, the generated sequence was aligned against the targeted T. reesei reference sequence (Table 1 and Table S3) [43].

2.2.2. Sensitivity Evaluation

Using DNA from the artificially synthetized control plasmid carrying a single copy of the targeted T. reesei sequence, serial dilutions were prepared ranging from 50 to 0 estimated target copy numbers. Each dilution point was then tested in 12 replicates using the developed real-time PCR TR method (Table 3). Based on the control plasmid size (3161 bp), the estimated target copy numbers for each dilution point were calculated, as previously described [20]. The limit of detection (LOD95%) was determined as previously described (Table S4) [20,44,45,46]. The plausibility check for the probability of detection (POD) curve presented no irregularities. Moreover, the POD curve was associated with a limit of detection (LOD95%) below 25 estimated target copies.

2.2.3. Applicability Evaluation

Several commercial food enzyme products were used to evaluate the applicability of the developed real-time PCR TR method (Table 4). For each sample (n°1–10), the real-time PCR TR method was applied in duplicate on 10 ng of DNA. These food enzyme products, in liquid or solid forms, were collected from different brands and are designed to be used in different sectors such as brewing, distillery, and baking. These food enzyme products were labeled as containing beta-glucanase, alpha-amylase, protease, cellulase, and xylanase. In samples n°1, 3–9, an unauthorized contamination with genetically modified bacterial strains was previously detected (RASFF2019.3332, RASFF2020.2577, RASFF2020.2579, and RASFF2020.2582). In addition to the real-time PCR TR method, these 10 food enzyme samples were also investigated for the presence of DNA from the Bacillus subtilis group, as previously described [22].

3. Results and Discussion

3.1. Development of the Real-Time PCR TR Method

According to the FEDA (Food Enzyme Database—accessed in May 2023), 150 food enzyme dossiers using GMMs are currently submitted for evaluation by EFSA. Of those food enzymes obtained from GMMs, 40% are produced by bacterial strains and 60% are produced by fungal strains. Among these fungal strains, the majority belong to only three species: A. niger (41.1%), T. reesei (27.8%), and A. oryzae (16.7%) [2,3,47]. The detection of such fungal species represents, therefore, a warning signal of possible contamination with producer organisms, including genetically modified strains, in food enzyme samples.
However, although more than half of the genetically modified microbial strains used to produce food enzymes belong to fungal species, the GMM detection strategy recently proposed that the control of GMM contamination in commercial microbial fermentation products nowadays only targets genetically modified bacterial strains. Moreover, to our knowledge, no real-time PCR method, being the most popular technology to control GMOs in the food and feed chain, was developed or validated to target these three key fungal species in commercial microbial fermentation products. Therefore, a taxon-specific real-time PCR targeting T. reesei was designed, developed, and validated in-house in this study.
Based on previous studies [37,38,39], the translation elongation factor alpha 1 (Tef1) gene from T. reesei was selected to develop the newly developed real-time PCR TR method (Table 1). Using the software Primer3, a set of primers and probes was designed, allowing for the amplification of 130 bp of the Tef1 gene.

3.2. Specificity Assessment of the Real-Time PCR TR Method

The specificity of the newly developed real-time PCR TR method was first confirmed in silico (Tables S1 and S2). On the one hand, in blasting the sequence generated by the real-time PCR TR method against the NCBI nucleotide collection (nr/nt) database, 30 hits of 100% in terms of coverage and identity were observed, all belonging to T. reesei (Table S1). Moreover, among all the fungal species genomes from the NCBI RefSeq Genome Database, a hit of 100% in terms of coverage and identity was only observed with T. reesei (Table S2). On the other hand, using SCREENED on a dataset composed of all Trichoderma sp. genome sequences extracted from the NCBI Genome database, a theoretical PCR amplification with the developed real-time PCR TR method was predicted only for T. reesei (Genbank: CP016234.1 T. reesei QM6a chromosome III).
The specificity of the newly developed real-time PCR TR method was then experimentally demonstrated using bacterial and fungal species often used by the food and feed industry to manufacture microbial fermentation products [2,3,15,16,17,18,19,20,21,22,47]. As positive controls, five WT T. reesei strains were used. As negative controls, 108 WT microbial strains and 2 genetically modified bacterial strains (RASFF2014.1249 and RASFF2019.3332) were used. In addition, one plant material and one animal material were tested. Among the 108 WT microbial strains, 43 bacterial species and 65 strains from 49 fungal species not belonging to T. reesei were included (Table 2). As expected, all the positive controls presented an amplification, while no amplification was observed for all the negative controls. Moreover, the sequence generated from the T. reesei IHEM 5264 strain, used as a positive control, showed 100% identity and coverage with the target T. reesei reference sequence (Table S3).
As a positive signal was exclusively detected in the samples containing the targeted T. reesei sequences and no false positive signals or false negative signals were reported, the developed real-time PCR TR method was consequently assessed as specific.

3.3. Sensitivity Assessment of the Real-Time PCR TR Method

The sensitivity of the newly developed real-time PCR TR method was assessed according to the international standard (ISO Standard 16140-2:2014). Using a control plasmid carrying a single copy of the target sequence from the T. reesei Tef1 gene, serial dilutions of DNA from the control plasmid, ranging from 50 to 0 estimated target copy numbers, were tested (Table 3).
At as low as 10 estimated target copies, an amplification signal was detected for all 12 replicates. Moreover, up to one estimated target copy, a positive signal was detected. Based on all positive and negative signals observed for all 12 replicates at each serial dilution point tested, the LOD95% of the real-time PCR TR method was calculated and established at eight estimated target copies (Table S4). Presenting an LOD95% lower than 25 estimated target copies, the newly developed real-time PCR TR method was assessed as sensitive.
This taxon-specific method is the first real-time PCR method designed to specifically screen for the presence of DNA from T. reesei in microbial fermentation products, with performance complying with the “MPR for Analytical Methods of GMO Testing” of the European Network of GMO Laboratories, which is the standard used by GMO enforcement laboratories [36].

3.4. Applicability Assessment of the Real-Time PCR TR Method

The applicability of the developed and in-house validated real-time PCR TR method was assessed using several commercial food enzyme products (Table 4). In liquid or solid forms, these products were collected from different brands and were intended for various sectors, such as brewing, distillery, and baking. These food enzyme products were labeled as containing beta-glucanase, alpha-amylase, protease, cellulase, or xylanase. Previously, all these food enzyme samples, except sample n°10, were reported for the presence of DNA specific to the B. subtilis group, using the real-time PCR BSG method (Table 4). In addition, contaminations of most of these samples (n°1, 3–9) with genetically modified Bacillus strains were identified previously (RASFF2019.3332, RASFF2020.2577, RASFF2020.2579, and RASFF2020.2582).
Among the 10 investigated food enzyme samples, the presence of DNA specific to T. reesei was detected in 6 samples (n°1–6) with an amplification signal above the LOD95% of the real-time PCR TR method (Table 4). For samples n°1–2, T. reesei was labeled as being the food enzyme-producing microbial species. These samples presented the lowest Cq values observed for the real-time PCR TR method. This is consistent with the product information available on the label. For sample n°4, a Cq value was also observed for the real-time PCR TR method, although T. reesei was not labeled as being the food enzyme-producing microbial species. For samples n°3, 5–6, only bacterial species, including from the Bacillus genus, were labeled as being the food enzyme-producing species. These samples showed low Cq values for the real-time PCR BSG method, in line with the labeled product information. However, a Cq value for the real-time PCR TR method was also observed. The origin of such T. reesei contaminations in samples n°3 and 5 could potentially be related to the production chain because these samples belong to the same brands as samples n°1 and 2, respectively. Regarding sample n°6, the origin of T. reesei contamination is unknown based on the available information. It could also be related to the production chain since mixes of food enzymes are manufactured using both Bacillus and Trichoderma species, as illustrated by sample n°1.
In 4 out of the 10 tested food enzyme samples (n°7–10), no T. reesei DNA was detected since either no amplification signal or an amplification signal below the LOD95% of the real-time PCR TR method were observed, indicating that no impurity with DNA from T. reesei was present (Table 4). For these four food enzyme samples, the labeling did not indicate that T. reesei was used for their manufacture. The food enzyme-producing microbial species were either labeled as belonging to the bacterial kingdom for sample n°7 or were non-labeled (unknown) for samples n°8–10.
According to all these results, the newly developed real-time PCR TR method was confirmed to be applicable to commercial food enzyme products. In addition, contamination with DNA specific to T. reesei with an amplification signal above the LOD95% was observed in several samples (n°1–6) (Table 4).

4. Conclusions

In this study, the real-time PCR TR method specific to the T. reesei species, whose genetically modified strains are widely used by the food industry to manufacture microbial fermentation products, was developed and validated in-house. This method was successfully evaluated as being specific since no false positive or false negative results were observed. In addition, in line with the “MPR for Analytical Methods of GMO Testing”, the method was assessed as being sensitive, allowing for the detection of T. reesei contaminations even at the trace level. Finally, the applicability of this real-time PCR method was demonstrated on several commercial microbial fermentation products. On this basis, the unexpected presence of DNA from T. reesei, genetically modified or not, was discovered, highlighting the relevance of this real-time PCR method to control unexpected biological impurities in the food and feed chain. In the future, additional real-time PCR methods specific to the A. niger and A. oryzae species, whose genetically modified strains are also frequently used by the food industry, could be developed to strengthen the control of unexpected fungal impurities in the food and feed chains. However, such real-time PCR methods allow only the screening of suspicious samples containing DNA specific to key fungal species. To clearly demonstrate the presence of genetically modified fungal strains, further investigations of the identified suspicious samples need to be performed to identify unnatural associations of sequences [19,48,49]. For this purpose, a whole-genome sequencing strategy may be considered. Here, a prior isolation of GMM strains, usually carried out by classical microbiology, is mandatory. However, for such GMMs used to produce microbial fermentation products, both bacterial and fungal strains, genetic information, including sequencing data, is confidential, which critically hampers the controls performed by enforcement laboratories to guarantee the traceability of commercial microbial fermentation products. Therefore, without publicly available information on the GMM strains of interest, this isolation step is particularly challenging due to the enormous list of microbial growth conditions to be tested, including possible auxotrophic mutations [15,48,50,51,52,53,54]. In the absence of prior knowledge, a high-throughput sequencing strategy, like metagenomics, represents an interesting and promising option, as recently demonstrated [49,55,56,57,58,59,60]. Nonetheless, metagenomics for the detection of GMMs in fermentation products is not yet mature enough to be implemented at the level of enforcement laboratories. In addition, its performance in terms of sensitivity is currently expected to be limited. To overcome this latter issue, a targeted sequencing strategy involving a prior enrichment step of key sequences is possible but consequently requires a minimum of publicly available information on the GMM strains used to manufacture fermentation products [19,20,61,62,63,64,65,66].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/fermentation9110926/s1, Table S1. Accession numbers from the NCBI Nucleotide collection database (nr/nt) presenting a hit of 100% in terms of identity and recovery with the sequence amplified by the real-time PCR TR method targeting T. reesei (Table 1); Table S2. Fungal species from the NCBI RefSeq Genome Database presenting a hit with the sequence amplified by the real-time PCR TR method targeting T. reesei (Table 1); Table S3. Sequence from the T. reesei IHEM5264 strain generated by the real-time PCR TR method aligned against the targeted reference Tef1 gene sequence from T. reesei (NW_006711153.1); Table S4: Calculation of LOD95% according to the POD curve for the newly developed real-time PCR TR method.

Author Contributions

Conceptualization, M.-A.F. and N.H.C.R.; methodology, M.-A.F. and N.H.C.R.; formal analysis, M.-A.F., A.G., N.P. and N.H.C.R.; writing—original draft preparation, M.-A.F.; writing—review and editing, M.-A.F., A.G., N.P. and N.H.C.R. All authors have read and agreed to the published version of the manuscript.

Funding

The research that yielded these results was funded by the Transversal activities in Applied Genomics (TAG) Service from Sciensano.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The Sanger sequencing was performed by TAG, Sciensano.

Conflicts of Interest

The authors declare no conflict 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.

References

  1. Raveendran, S.; Parameswaran, B.; Ummalyma, S.B.; Abraham, A.; Mathew, A.K.; Madhavan, A.; Rebello, S.; Pandey, A.; Biotechnology, J.R.G.C.F. Applications of Microbial Enzymes in Food Industry. Food Technol. Biotechnol. 2018, 56, 16–30. [Google Scholar] [CrossRef]
  2. Deckers, M.; Deforce, D.; Fraiture, M.-A.; Roosens, N.H. Genetically Modified Micro-Organisms for Industrial Food Enzyme Production: An Overview. Foods 2020, 9, 326. [Google Scholar] [CrossRef]
  3. Deckers, M.; Van Braekel, J.; Vanneste, K.; Deforce, D.; Fraiture, M.A.; Roosens, N.H.C. Food Enzyme Database (FEDA): A Web Application Gathering Information about Food Enzyme Preparations Available on the European Market. Database 2021, 2021, 1–7. [Google Scholar] [CrossRef]
  4. Lübeck, M.; Lübeck, P.S. Fungal Cell Factories for Efficient and Sustainable Production of Proteins and Peptides. Microorganisms 2022, 10, 753. [Google Scholar] [CrossRef]
  5. El-Gendi, H.; Saleh, A.K.; Badierah, R.; Redwan, E.M.; El-Maradny, Y.A.; El-Fakharany, E.M. A Comprehensive Insight into Fungal Enzymes: Structure, Classification, and Their Role in Mankind’s Challenges. J. Fungi 2021, 8, 23. [Google Scholar] [CrossRef] [PubMed]
  6. Iram, A.; Ozcan, A.; Turhan, I.; Demirci, A. Production of Value-Added Products as Food Ingredients via Microbial Fermentation. Processes 2023, 11, 1715. [Google Scholar] [CrossRef]
  7. Liu, X.; Ding, W.; Jiang, H. Engineering microbial cell factories for the production of plant natural products: From design principles to industrial-scale production. Microb. Cell Factories 2017, 16, 1–9. [Google Scholar] [CrossRef]
  8. Navarrete, C.; Jacobsen, I.H.; Martínez, J.L.; Procentese, A. Cell Factories for Industrial Production Processes: Current Issues and Emerging Solutions. Processes 2020, 8, 768. [Google Scholar] [CrossRef]
  9. Hussain, M.H.; Mohsin, M.Z.; Zaman, W.Q.; Yu, J.; Zhao, X.; Wei, Y.; Zhuang, Y.; Mohsin, A.; Guo, M. Multiscale engineering of microbial cell factories: A step forward towards sustainable natural products industry. Synth. Syst. Biotechnol. 2022, 7, 586–601. [Google Scholar] [CrossRef] [PubMed]
  10. Salazar-Cerezo, S.; de Vries, R.P.; Garrigues, S. Strategies for the Development of Industrial Fungal Producing Strains. J. Fungi 2023, 9, 834. [Google Scholar] [CrossRef] [PubMed]
  11. Boukid, F.; Ganeshan, S.; Wang, Y.; Tülbek, M.; Nickerson, M.T. Bioengineered Enzymes and Precision Fermentation in the Food Industry. Int. J. Mol. Sci. 2023, 24, 10156. [Google Scholar] [CrossRef] [PubMed]
  12. Bischof, R.H.; Ramoni, J.; Seiboth, B. Cellulases and beyond: The first 70 years of the enzyme producer Trichoderma reesei. Microb. Cell Factories 2016, 15, 1–13. [Google Scholar] [CrossRef] [PubMed]
  13. Barbau-Piednoir, E.; De Keersmaecker, S.C.J.; Delvoye, M.; Gau, C.; Philipp, P.; Roosens, N.H. Use of next generation sequencing data to develop a qPCR method for specific detection of EU-unauthorized genetically modified Bacillus subtilis overproducing riboflavin. BMC Biotechnol. 2015, 15, 103. [Google Scholar] [CrossRef]
  14. Barbau-Piednoir, E.; De Keersmaecker, S.C.J.; Wuyts, V.; Gau, C.; Pirovano, W.; Costessi, A.; Philipp, P.; Roosens, N.H. Genome Sequence of EU-Unauthorized Genetically Modified Bacillus subtilis Strain 2014-3557 Overproducing Riboflavin, Isolated from a Vitamin B2 80% Feed Additive. Genome Announc. 2015, 3, e00214-15. [Google Scholar] [CrossRef]
  15. Fraiture, M.-A.; Bogaerts, B.; Winand, R.; Deckers, M.; Papazova, N.; Vanneste, K.; De Keersmaecker, S.C.J.; Roosens, N.H.C. Identification of an unauthorized genetically modified bacteria in food enzyme through whole-genome sequencing. Sci. Rep. 2020, 10, 1–12. [Google Scholar] [CrossRef] [PubMed]
  16. Fraiture, M.-A.; Deckers, M.; Papazova, N.; Roosens, N.H. Detection strategy targeting a chloramphenicol resistance gene from genetically modified bacteria in food and feed products. Food Control. 2019, 108, 106873. [Google Scholar] [CrossRef]
  17. Fraiture, M.-A.; Deckers, M.; Papazova, N.; Roosens, N.H. Are antimicrobial resistance genes key targets to detect genetically modified microorganisms in fermentation products? Int. J. Food Microbiol. 2020, 331, 108749. [Google Scholar] [CrossRef]
  18. Fraiture, M.-A.; Deckers, M.; Papazova, N.; Roosens, N.H.C. Strategy to Detect Genetically Modified Bacteria Carrying Tetracycline Resistance Gene in Fermentation Products. Food Anal. Methods 2020, 13, 1929–1937. [Google Scholar] [CrossRef]
  19. Fraiture, M.-A.; Papazova, N.; Roosens, N.H. DNA walking strategy to identify unauthorized genetically modified bacteria in microbial fermentation products. Int. J. Food Microbiol. 2020, 337, 108913. [Google Scholar] [CrossRef]
  20. Fraiture, M.-A.; Gobbo, A.; Marchesi, U.; Verginelli, D.; Papazova, N.; Roosens, N.H. Development of a real-time PCR marker targeting a new unauthorized genetically modified microorganism producing protease identified by DNA walking. Int. J. Food Microbiol. 2021, 354, 109330. [Google Scholar] [CrossRef] [PubMed]
  21. Fraiture, M.-A.; Marchesi, U.; Verginelli, D.; Papazova, N.; Roosens, N.H.C. Development of a Real-time PCR Method Targeting an Unauthorized Genetically Modified Microorganism Producing Alpha-Amylase. Food Anal. Methods 2021, 14, 2211–2220. [Google Scholar] [CrossRef]
  22. Fraiture, M.A.; Gobbo, A.; Papazova, N.; Roosens, N.H.C. Development of a Taxon-Specific Real-Time PCR Method Targeting the Bacillus Subtilis Group to Strengthen the Control of Genetically Modified Bacteria in Fermentation Products. Fermentation 2022, 8, 78. [Google Scholar] [CrossRef]
  23. Jordan, K.; McAuliffe, O. Listeria Monocytogenes in Foods. In Advances in Food and Nutrition Research; Elsevier: Amsterdam, The Netherlands, 2018; Volume 86, pp. 181–213. ISBN 9780128139776. [Google Scholar]
  24. Nadeem, S.F.; Gohar, U.F.; Tahir, S.F.; Mukhtar, H.; Pornpukdeewattana, S.; Nukthamna, P.; Ali, A.M.M.; Bavisetty, S.C.B.; Massa, S. Antimicrobial resistance: More than 70 years of war between humans and bacteria. Crit. Rev. Microbiol. 2020, 46, 578–599. [Google Scholar] [CrossRef]
  25. von Wintersdorff, C.J.H.; Penders, J.; Van Niekerk, J.M.; Mills, N.D.; Majumder, S.; Van Alphen, L.B.; Savelkoul, P.H.M.; Wolffs, P.F.G. Dissemination of Antimicrobial Resistance in Microbial Ecosystems through Horizontal Gene Transfer. Front. Microbiol. 2016, 7, 173. [Google Scholar] [CrossRef]
  26. von Wright, A.; Bruce, Å. 7. Genetically modified microorganisms and their potential effects on human health and nutrition. Trends Food Sci. Technol. 2003, 14, 264–276. [Google Scholar] [CrossRef]
  27. Tóth, A.G.; Csabai, I.; Krikó, E.; Tőzsér, D.; Maróti, G.; Patai, Á.V.; Makrai, L.; Szita, G.; Solymosi, N. Antimicrobial resistance genes in raw milk for human consumption. Sci. Rep. 2020, 10, 1–7. [Google Scholar] [CrossRef] [PubMed]
  28. Vinayamohan, P.G.; Pellissery, A.J.; Venkitanarayanan, K. Role of horizontal gene transfer in the dissemination of antimicrobial resistance in food animal production. Curr. Opin. Food Sci. 2022, 47. [Google Scholar] [CrossRef]
  29. Dimitriu, T. Evolution of horizontal transmission in antimicrobial resistance plasmids. Microbiology 2022, 168, 001214. [Google Scholar] [CrossRef] [PubMed]
  30. Liu, G.; Thomsen, L.E.; Olsen, J.E. Antimicrobial-induced horizontal transfer of antimicrobial resistance genes in bacteria: A mini-review. J. Antimicrob. Chemother. 2021, 77, 556–567. [Google Scholar] [CrossRef]
  31. Jian, Z.; Zeng, L.; Xu, T.; Sun, S.; Yan, S.; Yang, L.; Huang, Y.; Jia, J.; Dou, T. Antibiotic resistance genes in bacteria: Occurrence, spread, and control. J. Basic Microbiol. 2021, 61, 1049–1070. [Google Scholar] [CrossRef]
  32. Michaelis, C.; Grohmann, E. Horizontal Gene Transfer of Antibiotic Resistance Genes in Biofilms. Antibiotics 2023, 12, 328. [Google Scholar] [CrossRef] [PubMed]
  33. Samtiya, M.; Matthews, K.R.; Dhewa, T.; Puniya, A.K. Antimicrobial Resistance in the Food Chain: Trends, Mechanisms, Pathways, and Possible Regulation Strategies. Foods 2022, 11, 2966. [Google Scholar] [CrossRef]
  34. Paloheimo, M.; Haarmann, T.; Mäkinen, S.; Vehmaanperä, J. Production of Industrial Enzymes in Trichoderma Reesei. In Gene Expression Systems in Fungi: Advancements and Applications; Schmoll, M., Dattenböck, C., Eds.; Fungal Biology; Springer International Publishing: Cham, Switzerland, 2016; pp. 23–57. ISBN 978-3-319-27949-7. [Google Scholar]
  35. Fischer, A.J.; Maiyuran, S.; Yaver, D.S. Industrial Relevance of Trichoderma Reesei as an Enzyme Producer. In Trichoderma Reesei; Mach-Aigner, A.R., Martzy, R., Eds.; Methods in Molecular Biology; Springer: New York, NY, USA, 2021; Volume 2234, pp. 23–43. ISBN 978-1-07-161047-3. [Google Scholar]
  36. Marchesi, U.; Mazzara, M.; Broll, H.; Giacomo, M.D.; Grohmann, L.; Herau, V.; Holst-Jensen, A.; Hougs, L.; Hübert, P.; Laurensse, E.; et al. European Network of GMO Laboratories (ENGL)—Definition of Minimum Perfor-mance Requirements for Analytical Methods of GMO Testing. JRC Rep. 2015, JRC95544. [Google Scholar] [CrossRef]
  37. Saroj, D.B.; Dengeti, S.N.; Aher, S.; Gupta, A.K. A rapid, one step molecular identification of Trichoderma citrinoviride and Trichoderma reesei. World J. Microbiol. Biotechnol. 2015, 31, 995–999. [Google Scholar] [CrossRef]
  38. Lücking, R.; Aime, M.C.; Robbertse, B.; Miller, A.N.; Ariyawansa, H.A.; Aoki, T.; Cardinali, G.; Crous, P.W.; Druzhinina, I.S.; Geiser, D.M.; et al. Unambiguous identification of fungi: Where do we stand and how accurate and precise is fungal DNA barcoding? IMA Fungus 2020, 11, 1–32. [Google Scholar] [CrossRef]
  39. Hinterdobler, W.; Li, G.; Spiegel, K.; Basyouni-Khamis, S.; Gorfer, M.; Schmoll, M. Trichoderma reesei Isolated From Austrian Soil With High Potential for Biotechnological Application. Front. Microbiol. 2021, 12. [Google Scholar] [CrossRef]
  40. Untergasser, A.; Cutcutache, I.; Koressaar, T.; Ye, J.; Faircloth, B.C.; Remm, M.; Rozen, S.G. Primer3—New capabilities and interfaces. Nucleic Acids Res. 2012, 40, e115. [Google Scholar] [CrossRef]
  41. Koressaar, T.; Remm, M. Enhancements and modifications of primer design program Primer3. Bioinformatics 2007, 23, 1289–1291. [Google Scholar] [CrossRef] [PubMed]
  42. Vanneste, K.; Garlant, L.; Broeders, S.; Van Gucht, S.; Roosens, N.H. Application of whole genome data for in silico evaluation of primers and probes routinely employed for the detection of viral species by RT-qPCR using dengue virus as a case study. BMC Bioinform. 2018, 19, 312. [Google Scholar] [CrossRef] [PubMed]
  43. Sievers, F.; Wilm, A.; Dineen, D.; Gibson, T.J.; Karplus, K.; Li, W.; Lopez, R.; McWilliam, H.; Remmert, M.; Söding, J.; et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 2011, 7, 539. [Google Scholar] [CrossRef]
  44. Grohmann, L.; Broll, H.; Dagand, E.; Hildebrandt, S.; Hübert, P.; Kiesecker, H.; Lieske, K.; Mäde, D.; Mankertz, J.; Reiting, R.; et al. Guidelines for the Validation of Qualitative Real-Time PCR Methods by Means of a Collabora-tive Study. Tech. Rep. 2016, BVL1. [Google Scholar]
  45. Uhlig, S.; Frost, K.; Colson, B.; Simon, K.; Mäde, D.; Reiting, R.; Gowik, P.; Grohmann, L. Validation of qualitative PCR methods on the basis of mathematical-statistical modelling of the probability of detection. Accredit. Qual. Assur. 2015, 20, 75–83. [Google Scholar] [CrossRef]
  46. Wehling, P.; A LaBudde, R.; Brunelle, S.L.; Nelson, M.T. Probability of Detection (POD) as a Statistical Model for the Validation of Qualitative Methods. J. AOAC Int. 2011, 94, 335–347. [Google Scholar] [CrossRef] [PubMed]
  47. Deckers, M.; De Loose, M.; Papazova, N.; Deforce, D.; Fraiture, M.-A.; Roosens, N.H. First monitoring for unauthorized genetically modified bacteria in food enzymes from the food market. Food Control. 2021, 135, 108665. [Google Scholar] [CrossRef]
  48. D’aes, J.; Fraiture, M.A.; Bogaerts, B.; De Keersmaecker, S.C.J.; Roosens, N.H.C.; Vanneste, K. Characterization of Genetically Modified Microorganisms Using Short- and Long-Read Whole-Genome Sequencing Reveals Con-taminations of Related Origin in Multiple Commercial Food Enzyme Products. Foods 2021, 10, 2637. [Google Scholar] [CrossRef] [PubMed]
  49. D’aes, J.; Fraiture, M.-A.; Bogaerts, B.; De Keersmaecker, S.C.J.; Roosens, N.H.C.J.; Vanneste, K. Metagenomic Characterization of Multiple Genetically Modified Bacillus Contaminations in Commercial Microbial Fermentation Products. Life 2022, 12, 1971. [Google Scholar] [CrossRef]
  50. Paracchini, V.; Petrillo, M.; Reiting, R.; Angers-Loustau, A.; Wahler, D.; Stolz, A.; Schönig, B.; Matthies, A.; Bendiek, J.; Meinel, D.M.; et al. Molecular characterization of an unauthorized genetically modified Bacillus subtilis production strain identified in a vitamin B 2 feed additive. Food Chem. 2017, 230, 681–689. [Google Scholar] [CrossRef]
  51. Zhang, R.; Yang, T.; Zhang, Q.; Liu, D.; Elhadidy, M.; Ding, T. Whole-genome sequencing: A perspective on sensing bacterial risk for food safety. Curr. Opin. Food Sci. 2022, 47. [Google Scholar] [CrossRef]
  52. Hadi, J.; Rapp, D.; Dhawan, S.; Gupta, S.K.; Gupta, T.B.; Brightwell, G. Molecular detection and characterization of foodborne bacteria: Recent progresses and remaining challenges. Compr. Rev. Food Sci. Food Saf. 2023, 22, 2433–2464. [Google Scholar] [CrossRef]
  53. Salem-Bango, Z.; Price, T.K.; Chan, J.L.; Chandrasekaran, S.; Garner, O.B.; Yang, S. Fungal Whole-Genome Sequencing for Species Identification: From Test Development to Clinical Utilization. J. Fungi 2023, 9, 183. [Google Scholar] [CrossRef]
  54. Jagadeesan, B.; Gerner-Smidt, P.; Allard, M.W.; Leuillet, S.; Winkler, A.; Xiao, Y.; Chaffron, S.; Van Der Vossen, J.; Tang, S.; Katase, M.; et al. The use of next generation sequencing for improving food safety: Translation into practice. Food Microbiol. 2018, 79, 96–115. [Google Scholar] [CrossRef] [PubMed]
  55. Zhou, Y.; Ren, M.; Zhang, P.; Jiang, D.; Yao, X.; Luo, Y.; Yang, Z.; Wang, Y. Application of Nanopore Sequencing in the Detection of Foodborne Microorganisms. Nanomaterials 2022, 12, 1534. [Google Scholar] [CrossRef] [PubMed]
  56. Kobus, R.; Abuín, J.M.; Müller, A.; Hellmann, S.L.; Pichel, J.C.; Pena, T.F.; Hildebrandt, A.; Hankeln, T.; Schmidt, B. A big data approach to metagenomics for all-food-sequencing. BMC Bioinform. 2020, 21, 1–15. [Google Scholar] [CrossRef]
  57. Banerjee, G.; Agarwal, S.; Marshall, A.; Jones, D.H.; Sulaiman, I.M.; Sur, S.; Banerjee, P. Application of advanced genomic tools in food safety rapid diagnostics: Challenges and opportunities. Curr. Opin. Food Sci. 2022, 47, 100886. [Google Scholar] [CrossRef]
  58. Imanian, B.; Donaghy, J.; Jackson, T.; Gummalla, S.; Ganesan, B.; Baker, R.C.; Henderson, M.; Butler, E.K.; Hong, Y.; Ring, B.; et al. The power, potential, benefits, and challenges of implementing high-throughput sequencing in food safety systems. NPJ Sci. Food 2022, 6, 1–6. [Google Scholar] [CrossRef]
  59. Billington, C.; Kingsbury, J.M.; Rivas, L. Metagenomics Approaches for Improving Food Safety: A Review. J. Food Prot. 2022, 85, 448–464. [Google Scholar] [CrossRef] [PubMed]
  60. Akaçin, I.; Ersoy, Ş.; Doluca, O.; Güngörmüşler, M. Comparing the significance of the utilization of next generation and third generation sequencing technologies in microbial metagenomics. Microbiol. Res. 2022, 264, 127154. [Google Scholar] [CrossRef]
  61. Debode, F.; Hulin, J.; Charloteaux, B.; Coppieters, W.; Hanikenne, M.; Karim, L.; Berben, G. Detection and identification of transgenic events by next generation sequencing combined with enrichment technologies. Sci. Rep. 2019, 9, 1–9. [Google Scholar] [CrossRef]
  62. Volpicella, M.; Leoni, C.; Costanza, A.; Fanizza, I.; Placido, A.; Ceci, L.R. Genome Walking by Next Generation Sequencing Approaches. Biology 2012, 1, 495–507. [Google Scholar] [CrossRef]
  63. Hess, J.; Kohl, T.; Kotrová, M.; Rönsch, K.; Paprotka, T.; Mohr, V.; Hutzenlaub, T.; Brüggemann, M.; Zengerle, R.; Niemann, S.; et al. Library preparation for next generation sequencing: A review of automation strategies. Biotechnol. Adv. 2020, 41, 107537. [Google Scholar] [CrossRef]
  64. Pei, X.M.; Yeung, M.H.Y.; Wong, A.N.N.; Tsang, H.F.; Yu, A.C.S.; Yim, A.K.Y.; Wong, S.C.C. Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases. Cells 2023, 12, 493. [Google Scholar] [CrossRef] [PubMed]
  65. Satam, H.; Joshi, K.; Mangrolia, U.; Waghoo, S.; Zaidi, G.; Rawool, S.; Thakare, R.P.; Banday, S.; Mishra, A.K.; Das, G.; et al. Next-Generation Sequencing Technology: Current Trends and Advancements. Biology 2023, 12, 997. [Google Scholar] [CrossRef] [PubMed]
  66. Singh, R.R. Target Enrichment Approaches for Next-Generation Sequencing Applications in Oncology. Diagnostics 2022, 12, 1539. [Google Scholar] [CrossRef] [PubMed]
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