Genomic and metabolomic insights into Trichoderma harzianum T9, a resilient biocontrol fungus from arid environments

Genomic and metabolomic insights into Trichoderma harzianum T9, a resilient biocontrol fungus from arid environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genomic and metabolomic insights into Trichoderma harzianum T9, a resilient biocontrol fungus from arid environments Francisco Vargas-Gasca, Enrique Pola-Sánchez, Ana Valeria García-Lartigue, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7456804/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The search for sustainable agricultural solutions to reduce pesticide use is increasingly urgent, particularly under the growing pressures of climate change. Microorganisms from extreme environments offer valuable potential for biocontrol applications due to their unique adaptive traits, enabling survival under conditions such as high temperature, salinity and water scarcity. While Trichoderma species are well-known biocontrol agents, many strains perform poorly in extreme soils with high salinity or alkaline pH. Here, we characterize Trichoderma harzianum T9, an isolate from the alkaline desert soils of Nuevo León, México, that demonstrates exceptional resilience. T. harzianum T9 displayed significantly greater biocontrol efficiency against phytopathogenic fungi from strawberry plants compared with other strains. Genome sequencing, phylogenomics and SNP-based variant analysis revealed numerous genes involved in secondary metabolism with elevated nucleotide substitution rates. Metabologenomics predicted chemical variations, primarily in peptaibols, and identified six additional compounds through biosynthetic gene cluster (BGC) prediction, likely contributing to its strong antifungal capacity. These findings position T. harzianum T9 as a promising biocontrol agent for managing phytopathogens in degraded soils, offering an eco-friendly approach for sustainable agriculture. The unique genomic and metabolic traits of T9 highlight the untapped potential of microorganisms from extreme environments in advancing innovative strategies for crop protection and soil restoration. Biological sciences/Biotechnology Biological sciences/Microbiology Biological sciences/Plant sciences Biocontrol Extreme environments Secondary metabolism Peptaibols Biosynthetic gene clusters Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Filamentous fungi of the genus Trichoderma are widely distributed across diverse ecosystems and are renowned for their rapid growth, substrate versatility, and antagonistic activity against phytopathogenic fungi [ 1 ]. Some species also establish beneficial associations with plants, promoting growth and nutrient uptake [ 2 – 4 ], which underscores their value as biocontrol agents in agriculture. Despite these advantages, many Trichoderma strains are sensitive to harsh soil conditions such as high salinity and alkaline pH, which restricts their application in challenging environments [ 5 ]. Microorganisms adapted to extreme habitats, including deserts, often display unique genomic and phenotypic traits shaped by selective pressures such as heat, water scarcity, and nutrient limitation. These adaptations, including altered cell membranes, stress-related proteins, and broad metabolic flexibility, make extremophilic fungi promising resources for biotechnology [ 6 ]. However, widely used commercial strains like T. harzianum T22 and T. atroviride IMI206040 show reduced performance in saline or alkaline soils, highlighting the need for novel strains capable of thriving under such conditions. In a previous study, we isolated Trichoderma strains from the alkaline desert soils of Mina, Nuevo León, México. Among them, T. harzianum T9 [ 7 ] demonstrated exceptional tolerance to pH levels up to 9 and improved plant resilience under water stress. These features position T9 as a promising candidate for biocontrol and soil remediation in extreme agricultural settings. Notably, Trichoderma atroviride is known to combat phytopathogens such as Fusarium , Botrytis , Rhizoctonia , and Alternaria [ 8 , 9 ]. However, the emergence of highly aggressive fungi resistant to both chemical fungicides and conventional biocontrol strains has created serious challenges. Strawberry producers in Mexico, for example, face devastating outbreaks caused by Macrophomina spp. and Neopestalotiopsis rosae , which can reduce yields by up to 50% [ 10 ]. Current management practices have proven insufficient, underscoring the urgent need for novel, robust biocontrol solutions. Here we present the desert-derived strain T. harzianum T9, which exhibits stronger inhibition of emergent strawberry pathogens than reference strains such as T. atroviride IMI206040 and T. harzianum M10 [ 11 , 12 ]. Using an evolutionary framework, we identified genetic variants in T9 linked to secondary metabolism, including changes in PKS1, prompting a detailed genomic and metabolomic analysis. This approach revealed differences in peptaibol production, one of the most abundant metabolites in T9 with potential antimicrobial properties, as well as six additional secondary metabolites detectable under standard laboratory conditions. Methods Trichoderma strains The T. harzianum T9 strain was previously isolated in a prior study [8]. We thawed this strain from glycerol stock stored at -80°C. The same for T. harzianum M10 and T. atroviride IMI206040. These two strains were donated by the laboratory of Dr. Louise Glass at UC Berkeley. Isolation and identification of pathogens The sampling for the isolation of phytopathgens was conducted in the Irapuato region, of the state of Guanajuato, México, to isolate pathogens at coordinates 20.792689 and -101.362693. Strawberry plants of the Fragaria x ananassa variety, exhibiting severe wilting, were collected. The plants were sorted to the laboratory, where leaf, crown, and root sections were excised. The tissues were rinsed three times in 50 mL of a 5% sodium hypochlorite solution, followed by three washes with sterile distilled water. The disinfected tissues were then placed on plates containing PDA medium, supplemented with 34 mg/mL chloramphenicol to eliminate bacterial contamination, and incubated for 48 hours at 28°C. The fungal colonies that grew were isolated by transferring a block from the edge of the colony onto fresh PDA plates. Axenic fungal cultures were then cultivated in PDB medium for 24 hours with agitation at 140 rpm and 28°C. The mycelium was collected by filtration, and the genomic DNA was extracted following the protocol described by Sambrook (2006) [14]. Outsourcing sequencing services performed fungal identification to the "Laboratorio de Servicios Genómicos" (LABSERGEN) at Langebio, Cinvestav, Irapuato, México. The extracted DNA was amplified by PCR using the ITS1 (TCCGTAGGTGAACCTGCGG) and ITS4 (TCCTCCGCTTATTGATATGC) primers [15]. The amplified fragments were sequenced bi-directionally using the Sanger technique and analyzed using the BLAST algorithm in the GenBank database, allowing for taxonomic identification based on sequence similarity percentages. The Neopestalioptosis rosae strain was kindly provided by Dr. Angel Rebollar-Alviter (Unpublished data). A second strain of Fusarium sp . was isolated by Dra. Elva Aréchiga from stored sorghum seeds. Confrontation assays Confrontation assays were performed on PDA medium to assess the antagonistic ability of T. harzianum T9. All fungal strains were re-streaked on PDA medium and incubated for 48 hours at 28°C before evaluation. After incubation, 5 mm diameter plugs were excised from the colony edges and placed at the center of fresh PDA plates, followed by another 48-hour incubation. For the antagonism assay, 5-mm plugs of actively growing mycelium were 1 cm from the plate edges on the PDA medium. In the case of Fusarium strains, the plugs were inoculated 48 hours before Trichoderma inoculation, as Fusarium strains grow more slowly than Trichoderma strains. In contrast, Macrophomina sp and N. rosae strains were inoculated simultaneously with Trichoderma strains. The PDA medium at pH 8.5 was adjusted using 15 mM 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) for interaction and growth assays. Plates were photographed every 24 hours, and colony areas were measured using ImageJ software. Statistical analyses were conducted using GraphPad software, version 8 [16]. Two biological replicates were performed, each with three technical replicates. Genome sequencing and assembly The genome of T. harzianum T9 was sequenced using the Illumina Novaseq platform (Illumina Inc., San Diego, CA, USA). Fragment libraries were prepared using the TruSeq Nano DNA preparation kit (Illumina Inc., San Diego, CA, USA) with paired end reads of 150 bp. Quality control assessment was performed using FastQC v0.11.5 [17], followed by adapter and short sequence removal using Trimmomatic v0.39 [18] (Bolger et al., 2014). The resulting clean reads were used for de novo assembly using SPAdes v.3.15.2 with k -mer values ranging from 21 to 127 [19]. To eliminate potential contaminant sequences, FCS-GX v0.5.5, a tool from the NCBI Foreign Contamination Screen (FCS) suite [20], was used. The assembly statistics were determined using QUAST [21], yielding a genome size of 39.85 Mb. Analysis with Benchmarking Universal Single-Copy Orthologs (BUSCOs) revealed genome completeness of 99.3%, according to the fungi lineage [22]. Structural and functional annotation Prior to annotation, contigs with less than 500bp and duplicated were filtered out, subsequently, the remaining contigs were sorted and renamed using a toolkit via funannotate (https://funannotate.readthedocs.io/en/latest/install.html). Transposable elements (TEs) were identified using Earl Grey TE annotation pipeline (v4.3.0) [23], with the Ascomycota library from Dfam release 3.7 [24]. The generated soft-mask genome was used for annotation with BRAKER2 [25], using as evidence a fungal-specific protein partition, obtained from OrthoDB v11 [26]. The annotation stats were generated with JCVI toolkit [27] via Galaxy Europe Server (The Galaxy Community, 2024). Functional annotation was carried out with eggNOG Mapper v4.5 [28] and InterProScan 5 [29]. CAZymes identification Carbohydrate-active enzymes (CAZymes) were identified by using the dbCAN3 server (https://bcb.unl.edu/dbCAN2/blast.php) [30]. First, only the longest protein isoform sequences obtained by BRAKER2 were selected for the analysis. Afterward, the protein file was submitted to dbCAN3 under the following configuration: protein sequence mode, HMMER (E-value 0.35), DIAMOND (E-value < 1e-102), and dbCAN_sub (E-value 0.35). To assure the accuracy of enzyme identification, the sequences that fulfilled the selected analysis criteria by HMMER, DIAMOND, and dbCAN_sub were chosen to determine the CAZymes classification. Phylogenetic analysis Phylogenetic reconstruction was determined by using 29 fungal genomes from public databases [31-46] and the T. harzianum T9 genome. The webpage links to the genomes that were obtained from open databases can be consulted in the Supplementary Table 1. All genomes were annotated de novo by using BRAKER2 [25]. Proteomes of the following strains: Fusarium graminearum , Fusarium oxysporum , T. harzianum T9, T. harzianum CBS, T. harzianum M10, T. harzianum T22, T. atroviride IMI206040 and Trichoderma hamatum FBL_587 were then filtered to retain only the longest isoform from each gene. These single-copy sequences were used to construct the phylogenetic tree through OrthoVenn3 [47]. Single-copy sequences were aligned with Muscle [48], and conserved regions were obtained with TrimAL [49]. A phylogenetic tree was inferred by the Maximum Likelihood using FastTree [50] and the JTT + CAT model. Fusarium species were added to the analysis as an external group. A Newick format was obtained, and it was further used as a reference for the ROADIES software [51], which was executed with the 30 genomes in an accurate and convergence mechanism mode to obtain a solid, well-defined roots phylogenetic tree. In RODIES software a GENE_COUNT of 2000, IDENTITY of 65%, and COVERAGE of 85% was established to optimize the analysis for fungal genomes. Sequence alignment and genetic variant identification Clean reads were aligned against the reference genomes of T. harzianum strains M10, T22, Th0179, Th3844, TR274, T. afroharzianum Th6 and T. atroviride IMI206040 using the BWA-MEM algorithm [52] via Bash command-line interface. SAMtools and BCFtools [53] were employed to sort, index, and identify SNPs in the alignment files. Variant filtering was subsequently performed using VCFtools [54,] to generate variant position files for each comparison between T. harzianum T9 and the reference genomes. To assess gene-level variability, the number of SNPs per gene was normalized by gene length or relative abundance using R. Statistical normalization was performed with boxplot analysis, using T. atroviride IMI206040 as the control to establish standard variation thresholds and remove bias. Genes with a percentage of variation exceeding the interquartile range were classified as highly divergent. Orthologous relationships between T9 and other harzianum clade strains were determined with OrthoFinder [55], based on protein sequence comparisons from each strain’s genome annotation, enabling the identification of shared genes and the assessment of whether T9 variants occurred in highly conserved genes. Functional annotation of orthogroups was performed with eggNOG-mapper [28], to interpret the potential biological impact of detected variants. Finally, divergent genes were analyzed within their evolutionary and functional context, evaluating whether they belonged to conserved orthogroups under selective pressure and identifying functional patterns linked to genetic variability. Metabolite extraction After 4 days of cultivation in Petri dishes with PDA medium, the strains were sectioned into 6 mm diameter plugs. Five of these plugs were added to vials. In each vial, 0.6 mL of the solvent mixture MeOH:CH 2 Cl 2 :EtOAc (1:2:3, v/v/v) + 0.1% formic acid was added. The vials were placed in an ultrasonic bath for 60 minutes. The supernatant was transferred to a 2 mL microtube and evaporated to dryness using an Eppendorf Concentrator®. The samples were then resuspended in 1 mL of MeOH (Optima™ LC/MS Grade, Fisher Chemical™) centrifuged at 15,000 rpm for 10 min, and transferred to 1.5 mL glass vials for LC-MS/MS analysis. LC-MS/MS analysis Untargeted LC-MS/MS metabolomics analyses of the extracts were performed on a Vanquish Duo UHPLC binary system (Thermo Fisher Scientific, Waltham, MA, USA) coupled to an IDX-Orbitrap Mass Spectrometer (Thermo Fisher Scientific). The chromatographic separation of the analytes was achieved using two different methods: Waters ACQUITY BEH C18 (10 cm × 2.1 mm, 1.7 μm) (Waters TM , Milford, MA, USA) column equipped with an ACQUITY BEH C18 guard column kept at 40°C. The mobile phase consisted of MilliQ water + 0.1% formic acid (v/v) (A) and acetonitrile + 0.1% formic acid (v/v) (B) (both sourced from HiPerSolv CHROMANORM®, HPLC and LC-MS grade, VWR Chemicals BDH®). The mobile phase gradient composition was as follows: 0-0.8 min 2% B, 0.8-3.3 min 2% to 5% B, 3.3-10 min 5% to 100% B, 10-11 min 100% B. The column was then re-equilibrated at 2% B for 2.7 min. Flow rate was set at 0.35 mL/min. The injection volume was 1µL. Waters ACQUITY BEH C18 (5 cm × 2.1 mm, 1.7 μm) column (Waters TM , Milford, MA, USA), equipped with an ACQUITY BEH C18 guard column kept at 70°C. Mobile phase consisted of H 2 O + 10 mM (v/v) ammonium acetate (adjusted to pH 9.2 with ammonium hydroxide) as eluent A and acetonitrile + 0.1% formic acid (v/v) as eluent B. Gradient elution was applied at a flow rate of 0.5 mL min -1 according to the following: 0-0.8 min 40% B, 0.8-6.5 min 40% to 100% B, 6.5-8.5 min 100% B. The column was then re-equilibrated at 40% B for 1.5 min. The injection volume was 1 uL. The MS measurements were performed using heated electrospray ionization (HESI) mode in positive and negative ion mode in the first method. The second method utilized heated electrospray ionization (HESI) in positive ion mode only. Both methods used a voltage of 3500 V in positive mode and 2500 V in negative mode, acquiring in full MS/MS spectra (Data dependent Acquisition-driven MS/MS, DDA) in the mass range of 70-1000 Da in the first method and 100-1500 Da in the second method. The mass resolution was set to 120,000 for full scan MS and 30,000 for MS/MS events. Precursor ions were fragmented by stepped High-energy Collision Dissociation (HCD) using collision energies of 20, 40, and 55. The automatic gain control (AGC) target value set at 4X10 5 for the full MS and 5X10 4 for the MS/MS spectral acquisition. LC-MS/MS data analysis For MS/MS dereplication MS/MS data were converted to mzXML format using the MS-Convert software, which is part of ProteoWizard (Palo Alto, CA, USA). Feature detection was performed with MZmine3 version 3.3.0 and 3.6.0 [56]. Default values were used on the Processing wizard and MoNA spectral library was added for compound annotation. The resulting feature table (.csv) and MS/MS spectra files (.mgf) were exported and used for the FBMN analysis on GNPS. For FBMN analysis the GNPS Super-Quick Start Interface was used and the resulting feature table (.csv), MS/MS spectra files (.mgf) exported from MZmine3. Comparative genomic insights into secondary metabolite biosynthetic gene clusters To assess the presence and evolutionary relationships of the BGC encoding peptaibols, across different Trichoderma species, we performed a phylogenomic analysis using a curated panel of Trichoderma genomes. For this, we employed an optimized version of the CORASON pipeline [57], specifically adapted for fungal genomes and BGC identification (fungison, available at https://github.com/WeMakeMolecules/fungison). The CORASON workflow integrates synteny-based gene cluster comparisons with phylogenetic reconstruction of core biosynthetic genes, allowing the identification of orthologous clusters across multiple genomes. Using this approach, we linked BGC sequences from each Trichoderma genome to phylogenetic trees of key biosynthetic enzymes, enabling us to infer the evolutionary relationships and potential conservation of these clusters across species. This methodology provided insights into the distribution and evolutionary trajectories of peptaibol biosynthetic pathways in Trichoderma , shedding light on their diversification within the genus. Results Biocontrol efficiency of the T. harzianum T9 strain. To analyze the antagonistic capacity of strain T. harzianum T9, as a characteristic related to its production of metabolites with antimicrobial activity, we selected phytopathogenic fungi reported to have negative effects on agricultural crops, to carry out direct confrontations in plate. Two of the strains were isolated from strawberry plants and were identified as Macrophomina sp ., with a 99.8% similarity and 100% overlap, and Fusarium sp . (designated as Fusarium UG), with a 99.58% similarity and 100% overlap (see Supplementary Figure 1). We included in the confrontations a second Fusarium sp . strain and the emerging pathogen N. rosae . Co-culture experiments revealed that T. harzianum T9 has better antagonistic activity than the well-known biocontrol Trichoderma strains, T. atroviride IMI 206040 and T. harzianum M10, which showed limited efficacy against the pathogens tested. The T. harzianum T9 strain significantly inhibited the growth of the four pathogens tested after 120 hours (Fig. 1A). Notably, exhibited significant biocontrol activity as early as 72 hours (Figure 1D, E). Furthermore, observations on the backside of petri dishes showed that T. harzianum T9 produced an orange-brown pigment, which appeared to invade and dominate the area occupied by the pathogenic fungi (Figure 1B). This strain was particularly effective against the aggressive pathogens M. sp and N. rosae (Figure 1C), which are among the most damaging pathogens, but not well-controlled effectively by other Trichoderma strains in strawberry fields. T. harzianum T9 demonstrates high biocontrol efficiency under extreme alkaline conditions In a previous study, we demonstrated that T. harzianum T9 is highly resistant to salinity, either low or high pH levels, and capable of promoting growth in sorghum plants [8]. This adaptability is likely due to its origin from an arid region, which may have driven its resilience. However, high stress tolerance might also provide an advantage when competing with pathogens, which often secrete compounds that either acidify or alkalize their environment. It is likely that T. harzianum T9 is a strong competitor against such pathogens even under extreme conditions. T. harzianum T9 grew effectively in alkaline media (pH 8.5), while the growth of the other two Trichoderma and pathogenic strains was significantly impaired at the same time point (Supplementary Figure 2). Interestingly, in the presence of a pH indicator (litmus, blue color), T. atroviride acidified the medium (turning it yellow), whereas T. harzianum T9 did not, despite its ability to grow (Supplementary Figure 2). This suggests that T. harzianum T9 may use alternative mechanisms to survive and thrive under these extreme conditions. T. harzianum T9 showed, at a pH of 8.5, high effectiveness in controlling M. tecta , unlike the other two Trichoderma strains (Figure 2A). This observation was consistent across three independent biological replicates, and the reduction in the diameter of M. sp was statistically significant by Tukey-HSD test (p < 0.05) when competing against T. harzianum T9 (Figure 2B). Notably, at this pH, T. harzianum T9 was able to overgrow the pathogenic strain, with a distinct necrotic area visible after 96 and 120 hours on the backside of the petri dishes. We observed the same in N. rosae at this pH (Fig. 2C). The graphs show significant degree of growth inhibition exerted by the T. harzianum T9 strain compared to that of the strains T. atroviride IMI 206040 and T. harzianum M10 (Figure 2D) . Assembly and prediction of genome elements To delve into the particularities of the T. harzianum T9 strain and try to correlate part of its physiology with its genetic characteristics, we decided to get its genome to look for traits linked to its activity. Therefore, we sequenced, assembled and annotated the genome. First, we used the illumina reads to obtain a genome profile using tools Jellyfish version 2.2.10 [58] and GenomeScope version 1 [59] to analyze k-mer frequencies from the genome reads. This analysis (Figure 3A), allowed us to estimate a genome size of 39.85 Mb, which was consistent with the genome assembly obtained in 134 contigs using SPADES. This genome has a GC content of 48.24%, an N50 of 903,272 (Supplementary Table 2), and an estimated completeness of 99.3%. These results confirm the high quality of the assembly (Figure 3B). The genome size and GC content of the T. harzianum T9 assembly were comparable to those reported for other T. harzianum strains sequenced using both short- and long-read technologies [60, 61, 62]. This suggests that the primary genomic characteristics of the species remain consistent. Additionally, the content TEs was analyzed using the Earl Grey tool [23]. This analysis revealed the presence of various types of TEs, including DNA transposons, LINEs (Long Interspersed Nuclear Elements), LTRs (Long Terminal Repeat retrotransposons), and other repetitive elements such as simple repeats, microsatellites, and RNA (Figure 3C). LTR retrotransposons were the most prevalent type of TEs, representing 2.30% of the genome (377 elements). In contrast, the largest number of individual elements belonged to the "Other" category, which included simple repeats, RNA, and microsatellites, accounting for 9851 elements and 1.07% of the genome. DNA transposons and LINE elements were less abundant, representing 0.18% and 0.45% of the genome, respectively. Overall, TEs constituted approximately 5.56% of the genome, distributed across 2661 distinct subcategories (Supplementary Table 3). Genome annotation Gene prediction in the assembled genome was performed using BRAKER2, resulting in 12,058 predicted genes, with an average gene size of 1,668 bp and a median gene size of 1,393 bp. The average exon size was 527 bp, and the median exon size was 284 bp (Figure 4A, C, D). These results are comparable to other Trichoderma genome annotations, where gene counts and BUSCO completeness statistics generally fall within a similar range, further supporting the robustness of the annotation. The quality of the annotation was assessed using BUSCO in transcriptome mode. To accomplish this, the transcripts of the genes predicted by BRAKER2 were extracted using GFFread v0.12.7 [63] and subjected to analysis. BUSCO results showed that 100% of the predicted gene transcripts were complete, with 96% identified as single-copy genes. No fragmented or missing transcripts were detected, indicating that the structural annotation of the genome is of high quality (Figure 4B). This indicates that we have an excellent assembly, covering the largest possible number of coding regions, which provides strong support for our future functional conclusions using this genome. CAZymes Carbohydrate-active enzymes (CAZymes) mediate the breakdown of plant cell walls, facilitating fungal interaction with the environment. In Trichoderma , they play a key role in mycoparasitism and organic matter degradation. Therefore, when analyzing a new Trichoderma genome, it is essential to predict how many and what types of CAZymes are encoded, as this reveals its ecological and biotechnological potential. The analysis of CAZymes, classified a total of 424 proteins as CAZymes, representing 3.5% of the 12,058 proteins used as input (Fig. S4A). Among these, 230 GH (glycoside hydrolases), 86 GT (glycosyltransferases), 51 AA (auxiliary activities), 20 CE (carbohydrate esterases), 8 PL (polysaccharide lyases), 1 CBM (carbohydrate-binding module), and 28 GH+CBM were identified. Out of the 424 CAZymes, 197 (46.5%) contained a signal peptide, suggesting that these proteins might be secreted (Fig. S4B; Supplementary Dataset 1). Phylogenetic analysis To move beyond the traditional descriptions based on phylogenies inferred from single molecular markers such as ITS, we generated a phylogenomic tree using all orthologous genes shared among 30 full-length genomes from other Hypocreales species. Out of the 30 genomes analyzed, 28 correspond to Trichoderma species, while the remaining two belong to Fusarium species, which were included as outgroups. All genomes underwent de novo annotation using BRAKER2. Prior to the phylogenetic analysis, genome annotations and quality were assessed using BUSCO in both genome and transcriptome modes. The BUSCO results demonstrated high-quality structural and functional annotations for most of the genomes (Supplementary Figure 3). The phylogenomic tree inference was performed with ROADIES [51] revealing robust clustering of the Trichoderma genus, supported by high bootstrap values, indicating strong evolutionary relationships among fungal species (Figure 5A). The phylogenetic tree showed well-defined clades, such as the group containing Trichoderma hamatum , Trichoderma asperellum , and T. atroviride , which displayed notable divergence from other genus members, suggesting specific evolutionary adaptations. Additionally, Trichoderma virens clustered with Trichoderma aggressivum , suggesting shared phenotypic and ecological traits. The clade comprising multiple T. harzianum strains included the T9 strain, which was most closely related to T. harzianum Th0179, while remaining within the harzianum group. This indicates a close evolutionary relationship among these strains. Our analysis clearly indicates that T9 belongs to T. harzianum . Additionally, we identified clusters of orthologous genes shared among T. harzianum strains (T9, M10 , T22, Th0179 and CBS). This analysis revealed a conserved core of 10,208 orthologous genes shared among all five strains, suggesting a highly conserved genetic basis (Figure 5B). Secondary metabolism genes show a high number of nucleotide changes Since the T. harzianum T9 strain shares many orthologs with other T. harzianum strains and containsrelatively few strain-specific genes (Fig. 6A), we hypothesized that most differences occur at thenucleotide level in gene variants. For this reason, we performed a variant analysis, comparing all orthologous genes between strain T9 and T. harzianum strains Th6, M10, T22, Th0179, Th3844, and TR274, as well as T. atroviride IMI206040 as a control due to its evolutionary distance, as shown in the phylogenetic tree in Figure 6A, where T. atroviride forms a separate clade from T. harzianum . Our initial SNP analysis revealed that the comparison between T. harzianum T9 and T. atroviride displayed a very broad variation, indicating that almost all genes exhibit changes, an expected result since they are different species. In contrast, comparisons between T. harzianum T9 and the other T. harzianum strains showed a narrower distribution, with an average of ~5% variation per gene (Fig. 6A). However, some genes appeared as outliers, showing more than 10% nucleotide changes across their coding sequences. We therefore focused on differences between T. harzianum T9 and the T. harzianum M10 genome, a T. harzianum strain that we previously used in competition assays against phytopathogenic fungi. In our initial inspection, certain genes clearly displayed a substantial number of SNPs throughout their coding regions (Fig. 6B). While some of these genes lacked functional annotation, it was noteworthy that several of the most divergent genes were involved in secondary metabolism, such as Glutathione S-transferase [64], which showed 13% variation, and a T1PKS [65], with 12% variation. Further annotation of the genes with the highest number of changes revealed the presence of enzymes relevant to secondary metabolite biosynthesis and modification (Fig. 6C), including a cytochrome P450, an enoyl reductase, which participates in both polyunsaturated fatty acid (PUFA) and polyketide biosynthesis in bacteria and fungi [66, 67], two short-chain dehydrogenase/reductases, typically components of large multi-domain enzymes such as mammalian fatty acid synthases or bacterial polyketide synthases [68], as well as an amidase, a beta-lactamase, an aldo/keto reductase, and an alpha/beta hydrolase. The high density of variants observed in the coding regions of these genes suggests a complex evolutionary scenario in which most changes may be selectively neutral, but a subset could be under positive selection, driving adaptive modifications. Given their association with secondary metabolism and the enhanced biocontrol ability of strain T. harzianum T9, these variants may contribute to increased production or diversification of bioactive metabolites, potentially leading to chemical innovations that improve competitive interactions with other organisms. This finding underscores the need for an in-depth functional characterization to identify which of these changes have adaptive value. BGC Diversity and Distribution Analysis To investigate whether T. harzianum T9 harbors novel biosynthetic pathways for metabolites that could contribute to the control of phytopathogenic fungi, we annotated the presence of 76 BGCs in its genome, using AntiSMASH (Figure 7). We then examined whether any of the genes with high nucleotide variation rates were part of the identified BGCs. As shown in Table 1, we found 16 genes with more than 10% nucleotide variation that are located within BGCs, including gene g11550, which encodes the core enzyme of cluster 8.2, a polyketide synthase (PKS) for which no associated metabolite has yet been identified, and gene g2166, the core of cluster 14.2, a nonribosomal peptide synthetase (NRPS) potentially involved in the biosynthesis of dichlorodiaporthin. Untargeted metabolomic analysis reveals divergent peptaibols in T. harzianum T9 Given that our genomic analysis suggests that many of the differences between T. harzianum T9 and other T. harzianum species may be related to secondary metabolism, we sought to characterize the chemical diversity produced under the growth conditions tested in this study. To profile the metabolites synthesized by T. harzianum T9, the strain was cultured on PDA plates, and the resulting metabolites were extracted for UHPLC-MS/MS analysis. The spectral data were processed using MZmine3 and submitted to Feature-Based Molecular Networking (FBMN), a workflow available through the Global Natural Products Social Molecular Networking (GNPS) platform. Compound annotations obtained from the FBMN workflow were then manually curated. We used BGC annotation data to corroborate the mass spectrometry-based structural annotations while assigning known or putative BGCs to most of the detected molecules. Among the annotated compounds, we found an 11-residue amino acid peptaibol (Harzianin HB I) with m/z of 1189.79 (Figure 8A), for which we annotated and proposed a chemical structure based on fragmentation patterns (figure 8B). MS-MS network analysis linked this ion to other less abundant molecules, including 14-residue amino acid peptaibols (Supplementary Figure 5). We proposed structures for the most abundant ions correspondent to m/z = 1175.7762 [M+H] + (Figure 8B), m/z =1189.7918 [M+H] + (Figure 8C), and m/z = 1442.9344 [M+H] + (Figure 8D; Supplementary Table 4). The MS2 fragment analysis showed several shared ions, indicating that the peptaibol products share most of their amino acid sequences which suggested that they are all derived from a single Non-Ribosomal Peptide Synthetase (NRPS) involving a module skipping mechanism. Such NRPS system (Tex2) has already been reported for other peptaibols in T. virens Gv29-8 [69]. Thus, T. harzianum T9 must have a NRPS featuring 14 modules, an N-terminal acyl loading module and a C-terminal reductase domain. Only one NRPS with this characteristic was found in the genome of T. harzianum T9, while no NRPS capable of generating 11-amino-acid peptaibols was found. A remarkable difference between T. harzianum T9 NRPS and Tex2 from T. virens is that the most abundant products of Tex2 are 14-membered peptaibols while for T. harzianum T9 the major product consist of 11-membered peptaibols. To investigate whether this BGC is present in other Trichoderma species and shed light on the evolution of these peptaibols, we performed a phylogenomic analysis using our panel of Trichoderma genomes. For this purpose, we used an improved version of the CORASON pipeline, optimized for fungal genomes and BGC (https://github.com/WeMakeMolecules/fungison). The analysis revealed that Tex2 from T. virens and the T9 NRPS (t9_Tex2) are orthologs, and that their BGC is highly syntenic among T. harzianum , T. virens , Trichoderma crassum , and T. aggressivum (Figure 9A) while in other lineages Tex2 orthologs are lost but the rest gene neighborhood remains conserved. We also identified additional secondary metabolites produced by T. harzianum T9 when grown under laboratory conditions on PDA medium, including harzianic acid (m/z = 364.1762; Fig. S6), harzianopyridone (m/z = 282.1340; Fig. S7), desferrichrocin (m/z = 718.3364; Fig. S8), pachybasin (m/z = 239.0705; Fig. S9), dimerumic acid (m/z = 485.2608; Fig. S10) and tricholignan (m/z = 221.1173; Fig. S11). Their structures were elucidated using biosynthetic gene clusters (BGCs) 19.2, 4.2, 11.1, 27.1, and 7.2 as references (Table S5). Discussion Most biocontrol agents currently used in agriculture have been isolated from agricultural soils [ 70 , 71 ], which are typically non-extreme and less challenging environments. It can be hypothesized that microorganisms from extreme environments not only develop mechanisms to resist environmental stresses, such as high temperatures, drought, UV radiation and osmotic stress, but also need to compete for the scarce nutrients available in these ecosystems. One case is that of microorganisms capable of thriving in deserts. Deserts impose multiple challenges, including physical barriers and competition with other microorganisms for limited nutrients. Therefore, these microorganisms must establish associations with plants to acquire nutrients such as photosynthates, while also effectively competing with other organisms for these resources. At the same time, they may promote plant growth and protect the host against pathogens, resulting in a commensal relationship. In the case of the genus Trichoderma , it is known that these fungi can parasitize other fungi [ 9 ], and the application of this mechanism is useful to control the growth of phytopathogenic fungi, which benefits agricultural crops [ 72 ]. However, with increasing temperatures due to climate change, several phytopathogenic fungi have developed more efficient competitive mechanisms [ 73 ] enabling them to resist attacks from commercially available Trichoderma species. The desert-derived strain T. harzianum T9, isolated from the rhizosphere of Agave lechuguilla in Nuevo León, Mexico [ 8 ], exhibited highly effective biocontrol activity against aggressive phytopathogenic fungi affecting strawberry crops in Mexico. For comparison, the performance of T. atroviride IMI206040 (widely recognized for its efficacy against pathogens of the genera Fusarium , Botrytis , and Alternaria [ 9 ]) and T. harzianum M10 was also evaluated. Both strains displayed considerably lower efficiency against strawberry-associated pathogens. These results indicate that the genetic repertoire of T. harzianum T9 provides a distinct competitive advantage, enabling it to surpass even other strains of the same species and suggesting specific genetic adaptations that underlie its superior biocontrol potential. To determine the genetic characteristics responsible of these properties in strain T. harzianum T9, we performed complete genome sequencing. Our phylogenomic analysis confirmed that T9 strain belongs to the harzianum species, validating previous ITS-based results [ 8 ]. Interestingly, T. harzianum T9 shares approximately 85% of orthologous genes with the harzianum clade, indicating that differences between strains may stem from a small number of specific genes and amino acid point mutations. Based on this, we conducted a species-wide polymorphism analysis using all high-quality T. harzianum genomes available. This approach allowed us to identify genes with the highest SNP density across their ORFs. Interestingly, many of the genes with elevated polymorphism rates were involved in secondary metabolism. Such a pattern has been previously reported in fungi; for instance, in the wheat pathogen Zymoseptoria tritici , a study analyzing 102 isolates found that significantly associated SNPs were mostly located in coding and gene regulatory regions, with the associated genes primarily encoding transport and catalytic activities [ 74 ]. This trend closely mirrors our observations in T. harzianum T9 when compared with other T. harzianum strains, where genes related to secondary metabolite biosynthesis exhibited a high frequency of nucleotide changes. These observations led us to hypothesize that selective pressures in desert environments have driven a specialization in the secondary metabolism profile of T. harzianum T9. For this reason, we set out to explore the metabolomic profile of this strain, aiming to identify the compounds it produces and to determine whether any metabolic variants could be detected. Through our metabologenomic analysis we were able to correlate several metabolites to their correspondent BGC. Interestingly, we were able to correlate seven molecules with their respective BGCs. Moreover, we observed that the most prominent peak corresponded to an 11-residue peptaibol, while a 14-residue peptaibol was also detected. Our evidence, along with a previous study by Mukherjee et al. , (2011), suggests that this is synthesized by the same NRPS but with certain skipped steps, resulting in a diversity of peptaibols. These molecules were previously identified in T. virens , a species highly virulent during mycoparasitism [ 69 , 75 ], suggesting that T. harzianum T9 may leverage this diversity of peptaibols as an advantage during mycoparasitism. Together, these findings indicate that, in addition to enhanced stress resistance capabilities, T. harzianum T9 can produce a wide variety of secondary metabolites that may contribute to its ability to inhibit the growth of phytopathogenic fungi. Moreover, we were surprised to find that, in this strain, the skipping system bypassing the synthesis of the 14-amino acid peptaibol was highly effective, leading to a significantly higher production of the 11-amino acid peptaibol. This could clearly contribute to differences in the biological activity of this compound. Further research is needed on this 11-residue peptaibol and the evolution of the skipping process in fungal NRPS. These metabolomic findings are highly relevant from an evolutionary perspective, as they suggest that certain fungi with strong biocontrol capabilities share chemical repertoires that enable them to perform this activity efficiently. However, it is particularly interesting that T. harzianum T9 exhibits an increased production of an 11-amino acid compound compared to Tex2 and presumably other T. harzianum strains. This may be due to modifications in transporters or even in the skipping system (Fig. 9 B), which could contribute to its remarkable biocontrol ability by synthesizing this peptaibol in great abundance. Such modifications, or even alterations in other yet unidentified metabolites, are also likely to contribute to the observed phenotype. The shift in synthesis preference from an 11 amino acid to a 14 amino acid molecule exemplifies the type of chemical variation that may arise. Given the extensive SNP profile observed, it is reasonable to expect that multiple additional variations of this kind occur, potentially influencing the metabolic capabilities and biocontrol properties of T. harzianum T9. Our in-depth phylogenomic analysis of Trichoderma genomes suggests that it is possible to identify the key mechanisms of competition and predict some secondary metabolic features from assembled genomes. This could represent a novel strategy to identify highly efficient fungal biocontrol agents based on their genetic characteristics. This approach is particularly relevant in the post-genomic era, where the cost of genome sequencing is relatively low, making routine genomic screening for new strains with desirable biotechnological applications feasible even before experimental validation. Conclusions Our study demonstrates that T. harzianum T9 is a highly versatile strain, capable of thriving in extreme environmental conditions while effectively controlling common strawberry pathogens in México. We propose this strain as a promising biocontrol agent for crops cultivated in high-pH soils and under drought stress. In addition, our genomic analysis enabled a detailed characterization of SNP distribution across the T. harzianum T9 genome, revealing specific changes within biosynthetic clusters. Notably, we observed alterations in the synthesis preferences of a peptaibol, underscoring the strain’s versatility in producing antifungal compounds. This biosynthetic pathway is also present in T. virens , a species recognized for its strong antifungal capacity. By identifying such patterns, our metabologenomics approach highlights its potential for characterizing novel strains with biocontrol potential. Declarations Additional Information Supplementary material is in the document SupplementaryFiguresT9.docx Competing interests The authors declare that they have no competing interests. Acknowledgements We thank Dr. Rebollar-Alviter for the kind donation of the N. rosae strain, Axel Sánchez-Fonseca, and Juan Ignacio Macías for their technical support in this work. We also thank Dr. Louise Glass at UC Berkeley for donating two model Trichoderma strains, which we used for comparison against our T. harzianum T9 strain. Author contributions J.M.V.E. and V.O.M. conceived and designed the intellectual content; F.V.G. conducted strain isolation experiments, genetic identification, and biocontrol assays. Additionally, they contributed to the manuscript writing; E.P.S. performed the sequencing experiments and genomic analyses presented in this study; A.V.G.L. performed the bioinformatic analyses for gene variant identification; A.V.G.L., A.D.G.V., P.C.M., A.C.C., D.R., L.A., and E.T.A.C. contributed to data collection, analysis, and manuscript revision; J.M.V.E. and V.O.M. secured funding for the project and drafted the manuscript; All authors have read and approved the final manuscript and agree to be responsible for all aspects of the work. Funding The work of ACC, PCM, DR was funded by the Novo Nordisk Foundation with grant NNF20CC0035580. The antagonism tests were carried out thanks to the resources derived from the CIIC 138/2025 project of the University of Guanajuato, under the responsibility of Vianey Olmedo Monfil. Data availability statement The genome assembly (FASTA) and annotation files for Trichoderma harzianum T9 have been deposited under NCBI BioProject PRJNA1231633 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1231633). The raw sequencing reads (FASTQ) have been deposited in Zenodo (https://zenodo.org/records/16968646). Code availability All codes used for the comparative genomics analyses are available in our GitHub repository at the following link: https://github.com/AnaValeriaG01/trichodermaT9 References Sariah, M., Choo, C. W., Zakaria, H. & Norihan, M. S. Quantification and characterisation of Trichoderma spp. from different ecosystems. Mycopathologia 159 , 113–117 (2005). Woo, S. L., Hermosa, R., Lorito, M. & Monte, E. 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Genome-wide association mapping reveals genes underlying population-level metabolome diversity in a fungal crop pathogen. BMC Biol. 20 , 224 (2022). Mukherjee, P. K., Horwitz, B. A. & Kenerley, C. M. Secondary metabolism in Trichoderma – a genomic perspective. Microbiology 158 , 35–45 (2012). Table Table 1. Genes with the highest number of nucleotide changes identified within biosynthetic gene clusters (BGCs). Percentage indicates the proportion of nucleotide changes normalized by gene length. BGC is the cluster identifier in the genome. Description is the functional annotation of the gene. fungiSMASH denotes the cluster family, and Compounds shows the predicted metabolite synthesized by each BGC. gene_id Percentage BGC Description FungiSMASH Compounds g7952 13.713 4.2 hypotetical protein terpene HEx-tc1 terpene g11550 12.36 8.2 T1PKS-core T1PKS unidentified g7546 11.466 37.2 Belongs to the metallo-dependent hydrolases superfamily. Peptidase M19 family NRPS verticillin g3049 10.304 18.1 hypotetical protein NRPS-like unidentified g3053 11.4 18.1 hypotetical protein NRPS-like unidentified g8005 11.212 4.3 Belongs to the short-chain dehydrogenases reductases (SDR) family T1PKS trichoxide g9221 11.096 5.1 hypotetical protein NRPS,T1PKS unidentified g7638 10.928 38.1 Calcineurin-like phosphoesterase terpene unidentified g9001 10.868 47.2 Protein kinase domain T1PKS unidentified g9011 10.187 47.2 Protein of unknown function (DUF3645) T1PKS unidentified g4667 10.822 22.1 Serine hydrolase (FSH1) T1PKS unidentified g4671 10.522 22.1 hypotetical protein T1PKS unidentified g9073 10.522 48.1 Acetyltransferase (GNAT) domain NRPS,T1PKS fujikurin A,B,C,D g7205 10.485 34.2 Carbon-nitrogen hydrolase NRPS,T1PKS unidentified g2166 10.262 14.2 NRPS-core NRPS,T1PKS dichlorodiaporthin g4025 10.234 2.4 GAL4-like Zn(II)2Cys6 (or C6 zinc) binuclear cluster DNA-binding domain terpene-precursor unidentified Additional Declarations No competing interests reported. Supplementary Files SupplementaryFiguresT9.docx SupplementaryDataset1.csv Cite Share Download PDF Status: Published Journal Publication published 08 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 30 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers invited by journal 10 Oct, 2025 Editor assigned by journal 22 Sep, 2025 Editor invited by journal 01 Sep, 2025 Submission checks completed at journal 27 Aug, 2025 First submitted to journal 27 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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(\u003cem\u003eM.sp\u003c/em\u003e), \u003cem\u003eN. rosae\u003c/em\u003e (\u003cem\u003eN.r\u003c/em\u003e), \u003cem\u003eFusarium\u003c/em\u003e sp. (\u003cem\u003eF.sp\u003c/em\u003e), and \u003cem\u003eFusarium UG\u003c/em\u003e (\u003cem\u003eF.UG\u003c/em\u003e) with the \u003cem\u003eTrichoderma\u003c/em\u003e strains \u003cem\u003eT. harzianum\u003c/em\u003e T9, \u003cem\u003eT. harzianum\u003c/em\u003e M10, and \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 (\u003cem\u003eT.a\u003c/em\u003e). Phytopathogens are positioned on the right side, while \u003cem\u003eTrichoderma\u003c/em\u003estrains are on the left side of the plate. The photographs were taken after 96 hours of interaction. (C) Close-up of the interaction zone between \u003cem\u003eM.sp\u003c/em\u003e or \u003cem\u003eN. r\u003c/em\u003e with the \u003cem\u003eTrichoderma\u003c/em\u003estrains T9 and M10. (D) Average colony area of \u003cem\u003eM.sp\u003c/em\u003e and (E) \u003cem\u003eN.r\u003c/em\u003e in interaction with different \u003cem\u003eTrichoderma\u003c/em\u003estrains. The control represents the average colony size of each pathogen growing alone. Asterisks indicate statistical differences according to the Tukey-HSD test at a significance level of α \u0026lt; 0.05, with *p \u0026lt; 0.05. n=3.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/8aa94c4010bb773d483609fa.png"},{"id":90510855,"identity":"eba42556-56a8-47ea-90c9-b3fb11bffd63","added_by":"auto","created_at":"2025-09-03 13:32:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":279246,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eT. harzianum\u003c/em\u003e T9 strain maintains its high antagonistic capacity under extreme alkaline conditions. (A) Top view of plates showing interactions between \u003cem\u003eT. harzianum\u003c/em\u003e T9, \u003cem\u003eT. harzianum\u003c/em\u003e M10, and \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 (\u003cem\u003eT.a\u003c/em\u003e) with \u003cem\u003eMacrophomina\u003c/em\u003e sp. (\u003cem\u003eM.sp\u003c/em\u003e) or (C) \u003cem\u003eN. rosae\u003c/em\u003e (\u003cem\u003eN.r\u003c/em\u003e), cultivated on PDA medium at pH 8.5. Phytopathogens are showed on the right side, while \u003cem\u003eTrichoderma\u003c/em\u003e strains are on the left. Photographs were taken after 96 hours of interaction. (B) Average colony area of \u003cem\u003eM.sp\u003c/em\u003e and (D) \u003cem\u003eN.r\u003c/em\u003e in interaction with different \u003cem\u003eTrichoderma\u003c/em\u003estrains. The control represents the average colony size of each pathogen growing alone. Asterisks indicate statistical differences according to the Tukey-HSD test at a significance level of α \u0026lt; 0.05, with *p \u0026lt; 0.05. n=3.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/fca1534450ec2652f5d1023c.png"},{"id":90511843,"identity":"856344cf-c011-4af0-ae4d-0ae8f0b3f44b","added_by":"auto","created_at":"2025-09-03 13:40:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":120870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic assembly of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eT. harzianum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e T9. \u003c/strong\u003e(A) K-mer spectra analysis using Jellyfish with k=21 for k-mer counting, and GenomeScope 1.0 for fitting models to estimate genome size, heterozygosity, and repetitiveness. (B) Assessment of assembly completeness using the BUSCO (Benchmarking Universal Single-Copy Orthologs) program with the fungi dataset. (C) Prediction and quantification of low complexity regions and repetitive elements in the genome.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/a93230ce6c8d06c4eb3f3861.png"},{"id":90512110,"identity":"3fb7d862-7b64-417e-8214-433592a0ab81","added_by":"auto","created_at":"2025-09-03 13:48:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":193356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural annotation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTrichoderma harzianum \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eT9 genome.\u003c/strong\u003e (A) Table of genome annotation statistics. (B) Completeness of the annotation using BUSCO (Benchmarking Universal Single-Copy Orthologs) with the fungi dataset, based on transcripts extracted with GFFread. (C) Gene length distribution. (D) Exon length distribution.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/962e63018be0a855715b5027.png"},{"id":90511845,"identity":"a9eca9b1-aa1f-4c2d-a8d8-d0fcadf62108","added_by":"auto","created_at":"2025-09-03 13:40:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192021,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis and orthologous clusters analysis in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTrichoderma\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e species and annotation of biosynthetic gene clusters (BGCs).\u003c/strong\u003e (A) Rooted phylogenetic tree of 30 fungal species generated using OrthoVenn3 and ROADIES. \u003cem\u003eFusarium oxysporum\u003c/em\u003e and \u003cem\u003eFusarium graminearum\u003c/em\u003e were used as outgroup species in the plot. (B) Venn diagram showing unique and shared orthologous clusters families among five \u003cem\u003eTrichoderma\u003c/em\u003e species, including the sequenced \u003cem\u003eT. harzianum\u003c/em\u003e T9, generated using OrthoVenn3.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/276c36478ddef47d09870e6a.png"},{"id":90511847,"identity":"718a8478-a253-4ecd-9994-0cb8bca7868a","added_by":"auto","created_at":"2025-09-03 13:40:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":250633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of nucleotide variation in T9 genes compared with other \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eT. harzianum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e strains. \u003c/strong\u003eA) Boxplot showing the percentage of SNPs in T9 orthologous genes compared with \u003cem\u003eT. harzianum\u003c/em\u003e strains Th6, M10, T22, Th0179, Th3844, and TR274, using \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 as a control. B) Scatterplot displaying the number of SNPs across orthologous genes in the comparison between T9 and the M10 strain. C) Annotation of genes with the highest SNP percentages (\u0026gt;11%) in the comparison of T9 versus M10.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/a325df59f67a1ecdc9694cd5.png"},{"id":90510858,"identity":"1610cd96-b8d4-449b-a93b-965490064d63","added_by":"auto","created_at":"2025-09-03 13:32:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":61636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of predicted BGCs by family in the genome of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eT. harzianum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e T9. \u003c/strong\u003eThis prediction was performed using fungiSMASH (the fungal analysis version of AntiSMASH). Ten cluster families were identified, with T1PKS, NRPS, and terpene being the three most abundant.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/b8050d1aeaa5fd3ee80d37b0.png"},{"id":90512111,"identity":"63a17f1c-51ba-42b2-8062-4841305dbc7a","added_by":"auto","created_at":"2025-09-03 13:48:58","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":81612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtracted ion chromatograms of m/z values referring to the peptaibols detected in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eT. harzianum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e T9 extracts cultivated in PDA medium.\u003c/strong\u003e A) Extracted ion chromatogram of (we have add here the m/z of each extracted ion chromatogram). and B) Fragmentation spectra of the precursor ion with m/z = 1175.7762 [M+H]\u003csup\u003e+\u003c/sup\u003e corresponding to 11-residue amino acid peptaibol. C) Fragmentation spectra of the precursor ion with m/z =1189.7918 [M+H]\u003csup\u003e+\u003c/sup\u003e corresponding to 11-residue amino acid peptaibol. D) Fragmentation spectra of the precursor ion with m/z =1442.9344 [M+H]\u003csup\u003e +\u003c/sup\u003e corresponding to 14-residue amino acid peptaibol.\u0026nbsp;\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/dca15c2731ead0025c3fab9c.png"},{"id":90510868,"identity":"5413067f-2eb9-4852-a1f0-f1901cd7c00d","added_by":"auto","created_at":"2025-09-03 13:32:58","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":299150,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMetabologenomic analysis of the most abundant BGCs producing peptaibols in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eT. harzianum\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eT9.\u003c/strong\u003e A) CORASON phylogenetic reconstruction using “gene-name” as the query gene and the \u003cem\u003eT.harzianum\u003c/em\u003eNRPS cluster responsible for 11- and 14-residue peptaibols. Genes absent in the reference cluster are highlighted and color-coded based on BLAST analysis. B) Biosynthetic pathway of the 12- and 14-residue peptaibols. The skipped residues during the biosynthesis process, caused by NRPS functionality, are highlighted in blue.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/174632ff8eb7e9b72142f11e.png"},{"id":100069663,"identity":"66dbb9dd-73a1-41bc-a48c-e82381e613c4","added_by":"auto","created_at":"2026-01-12 16:15:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3819116,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/905a0174-3d0c-4ed9-8551-7e6bf2d73cd9.pdf"},{"id":90510860,"identity":"460aa4a4-d5d1-4f13-9b23-f4b8cd3ee275","added_by":"auto","created_at":"2025-09-03 13:32:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1061020,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFiguresT9.docx","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/9eaa169be1a5efcc517b97eb.docx"},{"id":90510856,"identity":"aa4e9932-002b-4f8d-9364-75dab58d96e1","added_by":"auto","created_at":"2025-09-03 13:32:57","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22875,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataset1.csv","url":"https://assets-eu.researchsquare.com/files/rs-7456804/v1/49cbd555e9ab7acd630cb936.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic and metabolomic insights into Trichoderma harzianum T9, a resilient biocontrol fungus from arid environments","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFilamentous fungi of the genus \u003cem\u003eTrichoderma\u003c/em\u003e are widely distributed across diverse ecosystems and are renowned for their rapid growth, substrate versatility, and antagonistic activity against phytopathogenic fungi [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Some species also establish beneficial associations with plants, promoting growth and nutrient uptake [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], which underscores their value as biocontrol agents in agriculture. Despite these advantages, many \u003cem\u003eTrichoderma\u003c/em\u003e strains are sensitive to harsh soil conditions such as high salinity and alkaline pH, which restricts their application in challenging environments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMicroorganisms adapted to extreme habitats, including deserts, often display unique genomic and phenotypic traits shaped by selective pressures such as heat, water scarcity, and nutrient limitation. These adaptations, including altered cell membranes, stress-related proteins, and broad metabolic flexibility, make extremophilic fungi promising resources for biotechnology [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, widely used commercial strains like \u003cem\u003eT. harzianum\u003c/em\u003e T22 and \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 show reduced performance in saline or alkaline soils, highlighting the need for novel strains capable of thriving under such conditions.\u003c/p\u003e\u003cp\u003eIn a previous study, we isolated \u003cem\u003eTrichoderma\u003c/em\u003e strains from the alkaline desert soils of Mina, Nuevo Le\u0026oacute;n, M\u0026eacute;xico. Among them, \u003cem\u003eT. harzianum\u003c/em\u003e T9 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] demonstrated exceptional tolerance to pH levels up to 9 and improved plant resilience under water stress. These features position T9 as a promising candidate for biocontrol and soil remediation in extreme agricultural settings.\u003c/p\u003e\u003cp\u003eNotably, \u003cem\u003eTrichoderma atroviride\u003c/em\u003e is known to combat phytopathogens such as \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eBotrytis\u003c/em\u003e, \u003cem\u003eRhizoctonia\u003c/em\u003e, and \u003cem\u003eAlternaria\u003c/em\u003e [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the emergence of highly aggressive fungi resistant to both chemical fungicides and conventional biocontrol strains has created serious challenges. Strawberry producers in Mexico, for example, face devastating outbreaks caused by \u003cem\u003eMacrophomina\u003c/em\u003e spp. and \u003cem\u003eNeopestalotiopsis rosae\u003c/em\u003e, which can reduce yields by up to 50% [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Current management practices have proven insufficient, underscoring the urgent need for novel, robust biocontrol solutions.\u003c/p\u003e\u003cp\u003eHere we present the desert-derived strain \u003cem\u003eT. harzianum\u003c/em\u003e T9, which exhibits stronger inhibition of emergent strawberry pathogens than reference strains such as \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 and \u003cem\u003eT. harzianum\u003c/em\u003e M10 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Using an evolutionary framework, we identified genetic variants in T9 linked to secondary metabolism, including changes in PKS1, prompting a detailed genomic and metabolomic analysis. This approach revealed differences in peptaibol production, one of the most abundant metabolites in T9 with potential antimicrobial properties, as well as six additional secondary metabolites detectable under standard laboratory conditions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTrichoderma\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;strains\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eT. harzianum\u003c/em\u003e T9 strain was previously isolated in a prior study [8]. We thawed this strain from glycerol stock stored at -80\u0026deg;C. The same for \u003cem\u003eT. harzianum\u003c/em\u003e M10 and \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040. These two strains were donated by the laboratory of Dr. Louise Glass at UC Berkeley. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation and identification of pathogens\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sampling for the isolation of phytopathgens was conducted in the Irapuato region, of the state of Guanajuato, M\u0026eacute;xico, to isolate pathogens at coordinates 20.792689 and -101.362693. Strawberry plants of the \u003cem\u003eFragaria x ananassa\u003c/em\u003e variety, exhibiting severe wilting, were collected. The plants were sorted to the laboratory, where leaf, crown, and root sections were excised. The tissues were rinsed three times in 50 mL of a 5% sodium hypochlorite solution, followed by three washes with sterile distilled water. The disinfected tissues were then placed on plates containing PDA medium, supplemented with 34 mg/mL chloramphenicol to eliminate bacterial contamination, and incubated for 48 hours at 28\u0026deg;C.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe fungal colonies that grew were isolated by transferring a block from the edge of the colony onto fresh PDA plates. Axenic fungal cultures were then cultivated in PDB medium for 24 hours with agitation at 140 rpm and 28\u0026deg;C. The mycelium was collected by filtration, and the genomic DNA was extracted following the protocol described by Sambrook (2006) [14]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOutsourcing sequencing services performed fungal identification to the \u0026quot;Laboratorio de Servicios Gen\u0026oacute;micos\u0026quot; (LABSERGEN) at Langebio, Cinvestav, Irapuato, M\u0026eacute;xico. The extracted DNA was amplified by PCR using the ITS1 (TCCGTAGGTGAACCTGCGG) and ITS4 (TCCTCCGCTTATTGATATGC) primers [15]. The amplified fragments were sequenced bi-directionally using the Sanger technique and analyzed using the BLAST algorithm in the GenBank database, allowing for taxonomic identification based on sequence similarity percentages. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eNeopestalioptosis rosae\u003c/em\u003e strain was kindly provided by Dr. Angel Rebollar-Alviter (Unpublished data). A second strain of \u003cem\u003eFusarium sp\u003c/em\u003e. was isolated by Dra. Elva Ar\u0026eacute;chiga from stored sorghum seeds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfrontation assays\u003c/strong\u003e\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConfrontation assays were performed on PDA medium to assess the antagonistic ability of \u003cem\u003eT. harzianum\u003c/em\u003e T9. All fungal strains were re-streaked on PDA medium and incubated for 48 hours at 28\u0026deg;C before evaluation. After incubation, 5 mm diameter plugs were excised from the colony edges and placed at the center of fresh PDA plates, followed by another 48-hour incubation. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the antagonism assay, 5-mm plugs of actively growing mycelium were 1 cm from the plate edges on the PDA medium. In the case of \u003cem\u003eFusarium\u003c/em\u003e strains, the plugs were inoculated 48 hours before \u003cem\u003eTrichoderma\u003c/em\u003e inoculation, as \u003cem\u003eFusarium\u003c/em\u003e strains grow more slowly than \u003cem\u003eTrichoderma\u003c/em\u003e strains. In contrast, \u003cem\u003eMacrophomina\u003c/em\u003e \u003cem\u003esp\u003c/em\u003e and \u003cem\u003eN. rosae\u003c/em\u003e strains were inoculated simultaneously with \u003cem\u003eTrichoderma\u003c/em\u003e strains. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe PDA medium at pH 8.5 was adjusted using 15 mM \u003cem\u003e4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid\u003c/em\u003e (HEPES) for interaction and growth assays. Plates were photographed every 24 hours, and colony areas were measured using ImageJ software. Statistical analyses were conducted using GraphPad software, version 8 [16]. Two biological replicates were performed, each with three technical replicates. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome sequencing and assembly\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe genome of \u003cem\u003eT. harzianum\u003c/em\u003e T9 was sequenced using the Illumina Novaseq platform (Illumina Inc., San Diego, CA, USA). Fragment libraries were prepared using the TruSeq Nano DNA preparation kit (Illumina Inc., San Diego, CA, USA) with paired end reads of 150 bp. Quality control assessment was performed using FastQC v0.11.5 [17], followed by adapter and short sequence removal using Trimmomatic v0.39 [18] (Bolger \u003cem\u003eet al.,\u003c/em\u003e 2014). The resulting clean reads were used for \u003cem\u003ede novo\u003c/em\u003e assembly using SPAdes v.3.15.2 with \u003cem\u003ek\u003c/em\u003e-mer values ranging from 21 to 127 [19]. \u0026nbsp;To eliminate potential contaminant sequences, FCS-GX v0.5.5, a tool from the NCBI Foreign Contamination Screen (FCS) suite [20], was used. The assembly statistics were determined using QUAST [21], yielding a genome size of 39.85 Mb. Analysis with Benchmarking Universal Single-Copy Orthologs (BUSCOs) revealed genome completeness of 99.3%, according to the fungi lineage [22].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural and functional annotation\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior to annotation, contigs with less than 500bp and duplicated were filtered out, subsequently, the remaining contigs were sorted and renamed using a toolkit via funannotate (https://funannotate.readthedocs.io/en/latest/install.html). Transposable elements (TEs) were identified using Earl Grey TE annotation pipeline (v4.3.0) [23], with the Ascomycota library from Dfam release 3.7 [24]. The generated soft-mask genome was used for annotation with BRAKER2 [25], using as evidence a fungal-specific protein partition, obtained from OrthoDB v11 [26]. The annotation stats were generated with JCVI toolkit [27] via Galaxy Europe Server (The Galaxy Community, 2024). Functional annotation was carried out with eggNOG Mapper v4.5 [28] and InterProScan 5 [29]. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAZymes identification\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCarbohydrate-active enzymes (CAZymes) were identified by using the dbCAN3 server (https://bcb.unl.edu/dbCAN2/blast.php) [30]. \u0026nbsp;First, only the longest protein isoform sequences obtained by BRAKER2 were selected for the analysis. Afterward, the protein file was submitted to dbCAN3 under the following configuration: protein sequence mode, HMMER (E-value \u0026lt; 1e-15, coverage \u0026gt; 0.35), DIAMOND (E-value \u0026lt; 1e-102), and dbCAN_sub (E-value \u0026lt; 1e-15, coverage \u0026gt; 0.35). To assure the accuracy of enzyme identification, the sequences that fulfilled the selected analysis criteria by HMMER, DIAMOND, and dbCAN_sub were chosen to determine the CAZymes classification. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePhylogenetic reconstruction was determined by using 29 fungal genomes from public databases [31-46] and the \u003cem\u003eT. harzianum\u003c/em\u003e T9 genome. The webpage links to the genomes that were obtained from open databases can be consulted in the Supplementary Table 1. All genomes were annotated \u003cem\u003ede novo\u003c/em\u003e by using BRAKER2 [25]. Proteomes of the following strains: \u003cem\u003eFusarium graminearum\u003c/em\u003e, \u003cem\u003eFusarium oxysporum\u003c/em\u003e, \u003cem\u003eT. harzianum\u003c/em\u003e T9, \u003cem\u003eT. harzianum\u003c/em\u003e CBS, \u003cem\u003eT. harzianum\u003c/em\u003e M10, \u003cem\u003eT. harzianum\u003c/em\u003e T22, \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 and \u003cem\u003eTrichoderma hamatum\u003c/em\u003e FBL_587 were then filtered to retain only the longest isoform from each gene. These single-copy sequences were used to construct the phylogenetic tree through OrthoVenn3 [47]. Single-copy sequences were aligned with Muscle [48], and conserved regions were obtained with TrimAL [49]. A phylogenetic tree was inferred by the Maximum Likelihood using FastTree [50] and the JTT + CAT model. \u003cem\u003eFusarium\u003c/em\u003e species were added to the analysis as an external group. A Newick format was obtained, and it was further used as a reference for the ROADIES software [51], which was executed with the 30 genomes in an accurate and convergence mechanism mode to obtain a solid, well-defined roots phylogenetic tree. In RODIES software a GENE_COUNT of 2000, IDENTITY of 65%, and COVERAGE of 85% was established to optimize the analysis for fungal genomes. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequence alignment and genetic variant identification\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClean reads were aligned against the reference genomes of \u003cem\u003eT. harzianum\u003c/em\u003e strains M10, T22, Th0179, Th3844, TR274, \u003cem\u003eT. afroharzianum\u003c/em\u003e Th6 and \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 using the BWA-MEM algorithm [52] via Bash command-line interface. SAMtools and BCFtools [53] were employed to sort, index, and identify SNPs in the alignment files. Variant filtering was subsequently performed using VCFtools [54,] to generate variant position files for each comparison between \u003cem\u003eT. harzianum\u003c/em\u003e T9 and the reference genomes. To assess gene-level variability, the number of SNPs per gene was normalized by gene length or relative abundance using R. Statistical normalization was performed with boxplot analysis, using \u003cem\u003eT. atroviride\u0026nbsp;\u003c/em\u003eIMI206040 as the control to establish standard variation thresholds and remove bias. Genes with a percentage of variation exceeding the interquartile range were classified as highly divergent. Orthologous relationships between T9 and other \u003cem\u003eharzianum\u003c/em\u003e clade strains were determined with \u003cem\u003eOrthoFinder\u0026nbsp;\u003c/em\u003e[55], based on protein sequence comparisons from each strain\u0026rsquo;s genome annotation, enabling the identification of shared genes and the assessment of whether T9 variants occurred in highly conserved genes. Functional annotation of orthogroups was performed with \u003cem\u003eeggNOG-mapper\u003c/em\u003e [28], to interpret the potential biological impact of detected variants. Finally, divergent genes were analyzed within their evolutionary and functional context, evaluating whether they belonged to conserved orthogroups under selective pressure and identifying functional patterns linked to genetic variability. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetabolite extraction\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter 4 days of cultivation in Petri dishes with PDA medium, the strains were sectioned into 6 mm diameter plugs. Five of these plugs were added to vials. In each vial, 0.6 mL of the solvent mixture MeOH:CH\u003csub\u003e2\u003c/sub\u003eCl\u003csub\u003e2\u003c/sub\u003e:EtOAc (1:2:3, v/v/v) + 0.1% formic acid was added. The vials were placed in an ultrasonic bath for 60 minutes. The supernatant was transferred to a 2 mL microtube and evaporated to dryness using an Eppendorf Concentrator\u0026reg;. The samples were then resuspended in 1 mL of MeOH (Optima\u0026trade; LC/MS Grade, Fisher Chemical\u0026trade;) centrifuged at 15,000 rpm for 10 min, and transferred to 1.5 mL glass vials for LC-MS/MS analysis. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS analysis\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUntargeted LC-MS/MS metabolomics analyses of the extracts were performed on a Vanquish Duo UHPLC binary system (Thermo Fisher Scientific, Waltham, MA, USA) coupled to an IDX-Orbitrap Mass Spectrometer (Thermo Fisher Scientific). The chromatographic separation of the analytes was achieved using two different methods:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eWaters ACQUITY BEH C18 (10 cm \u0026times; 2.1 mm, 1.7 \u0026mu;m) (Waters\u003csup\u003eTM\u003c/sup\u003e, Milford, MA, USA) column equipped with an ACQUITY BEH C18 guard column kept at 40\u0026deg;C. \u0026nbsp;The mobile phase consisted of MilliQ water + 0.1% formic acid (v/v) (A) and acetonitrile + 0.1% formic acid (v/v) (B) (both sourced from HiPerSolv CHROMANORM\u0026reg;, HPLC and LC-MS grade, VWR Chemicals BDH\u0026reg;). The mobile phase gradient composition was as follows: 0-0.8 min 2% B, 0.8-3.3 min 2% to 5% B, 3.3-10 min 5% to 100% B, 10-11 min 100% B. The column was then re-equilibrated at 2% B for 2.7 min. Flow rate was set at 0.35 mL/min. The injection volume was 1\u0026micro;L. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWaters ACQUITY BEH C18 (5 cm \u0026times; 2.1 mm, 1.7 \u0026mu;m) column (Waters\u003csup\u003eTM\u003c/sup\u003e, Milford, MA, USA), equipped with an ACQUITY BEH C18 guard column kept at 70\u0026deg;C. Mobile phase consisted of H\u003csub\u003e2\u003c/sub\u003eO + 10 mM (v/v) ammonium acetate (adjusted to pH 9.2 with ammonium hydroxide) as eluent A and acetonitrile + 0.1% formic acid (v/v) as eluent B. Gradient elution was applied at a flow rate of 0.5 mL min\u003csup\u003e-1\u003c/sup\u003e according to the following: 0-0.8 min 40% B, 0.8-6.5 min 40% to 100% B, 6.5-8.5 min 100% B. The column was then re-equilibrated at 40% B for 1.5 min. The injection volume was 1 uL. \u0026nbsp;\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe MS measurements were performed using heated electrospray ionization (HESI) mode in positive and negative ion mode in the first method. The second method utilized heated electrospray ionization (HESI) in positive ion mode only. Both methods used a voltage of 3500 V in positive mode and 2500 V in negative mode, acquiring in full MS/MS spectra (Data dependent Acquisition-driven MS/MS, DDA) in the mass range of 70-1000 Da in the first method and 100-1500 Da in the second method. The mass resolution was set to 120,000 for full scan MS and 30,000 for MS/MS events. Precursor ions were fragmented by stepped High-energy Collision Dissociation (HCD) using collision energies of 20, 40, and 55. The automatic gain control (AGC) target value set at 4X10\u003csup\u003e5\u003c/sup\u003e for the full MS and 5X10\u003csup\u003e4\u003c/sup\u003e for the MS/MS spectral acquisition. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLC-MS/MS data analysis\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor MS/MS dereplication MS/MS data were converted to mzXML format using the MS-Convert software, which is part of ProteoWizard (Palo Alto, CA, USA). Feature detection was performed with MZmine3 version 3.3.0 and 3.6.0 [56]. Default values were used on the Processing wizard and MoNA spectral library was added for compound annotation. The resulting feature table (.csv) and MS/MS spectra files (.mgf) were exported and used for the FBMN analysis on GNPS. For FBMN analysis the GNPS Super-Quick Start Interface was used and the resulting feature table (.csv), MS/MS spectra files (.mgf) exported from MZmine3. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparative genomic insights into secondary metabolite biosynthetic gene clusters\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess the presence and evolutionary relationships of the BGC encoding peptaibols, across different \u003cem\u003eTrichoderma\u003c/em\u003e species, we performed a phylogenomic analysis using a curated panel of \u003cem\u003eTrichoderma\u003c/em\u003e genomes. For this, we employed an optimized version of the CORASON pipeline [57], specifically adapted for fungal genomes and BGC identification (fungison, available at https://github.com/WeMakeMolecules/fungison).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe CORASON workflow integrates synteny-based gene cluster comparisons with phylogenetic reconstruction of core biosynthetic genes, allowing the identification of orthologous clusters across multiple genomes. Using this approach, we linked BGC sequences from each \u003cem\u003eTrichoderma\u0026nbsp;\u003c/em\u003egenome to phylogenetic trees of key biosynthetic enzymes, enabling us to infer the evolutionary relationships and potential conservation of these clusters across species.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis methodology provided insights into the distribution and evolutionary trajectories of peptaibol biosynthetic pathways in \u003cem\u003eTrichoderma\u003c/em\u003e, shedding light on their diversification within the genus.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBiocontrol efficiency of the \u003cem\u003eT. harzianum\u003c/em\u003e T9 strain.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo analyze the antagonistic capacity of strain \u003cem\u003eT. harzianum\u003c/em\u003e T9, as a characteristic related to its production of metabolites with antimicrobial activity, we selected phytopathogenic fungi reported to have negative effects on agricultural crops, to carry out direct confrontations in plate. Two of the strains were isolated from strawberry plants and were identified as \u003cem\u003eMacrophomina sp\u003c/em\u003e., with a 99.8% similarity and 100% overlap, and \u003cem\u003eFusarium sp\u003c/em\u003e. (designated as Fusarium UG), with a 99.58% similarity and 100% overlap (see Supplementary Figure 1). We included in the confrontations a second \u003cem\u003eFusarium sp\u003c/em\u003e. strain and the emerging pathogen \u003cem\u003eN. rosae\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCo-culture experiments revealed that \u003cem\u003eT. harzianum\u003c/em\u003e T9 has better antagonistic activity than the well-known biocontrol \u003cem\u003eTrichoderma\u003c/em\u003e strains, \u003cem\u003eT. atroviride\u003c/em\u003e IMI 206040 and \u003cem\u003eT. harzianum\u003c/em\u003e M10, which showed limited efficacy against the pathogens tested. The\u003cem\u003e\u0026nbsp;T. harzianum\u003c/em\u003e T9 strain significantly inhibited the growth of the four pathogens tested after 120 hours (Fig. 1A). Notably, exhibited significant biocontrol activity as early as 72 hours (Figure 1D, E). Furthermore, observations on the backside of petri dishes showed that \u003cem\u003eT. harzianum\u003c/em\u003e T9 produced an orange-brown pigment, which appeared to invade and dominate the area occupied by the pathogenic fungi (Figure 1B). This strain was particularly effective against the aggressive pathogens \u003cem\u003eM. sp\u003c/em\u003e and \u003cem\u003eN. rosae\u003c/em\u003e (Figure 1C), which are among the most damaging pathogens, but not well-controlled effectively by other \u003cem\u003eTrichoderma\u003c/em\u003e strains in strawberry fields.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eT. harzianum\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;T9 demonstrates high biocontrol efficiency under extreme alkaline conditions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn a previous study, we demonstrated that \u003cem\u003eT. harzianum\u003c/em\u003e T9 is highly resistant to salinity, either low or high pH levels, and capable of promoting growth in sorghum plants [8]. This adaptability is likely due to its origin from an arid region, which may have driven its resilience. However, high stress tolerance might also provide an advantage when competing with pathogens, which often secrete compounds that either acidify or alkalize their environment. It is likely that \u003cem\u003eT. harzianum\u003c/em\u003e T9 is a strong competitor against such pathogens even under extreme conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT. harzianum\u003c/em\u003e T9 grew effectively in alkaline media (pH 8.5), while the growth of the other two \u003cem\u003eTrichoderma\u003c/em\u003e and pathogenic strains was significantly impaired at the same time point (Supplementary Figure 2). Interestingly, in the presence of a pH indicator (litmus, blue color), \u003cem\u003eT. atroviride\u003c/em\u003e acidified the medium (turning it yellow), whereas \u003cem\u003eT. harzianum\u003c/em\u003e T9 did not, despite its ability to grow (Supplementary Figure 2). This suggests that \u003cem\u003eT. harzianum\u003c/em\u003e T9 may use alternative mechanisms to survive and thrive under these extreme conditions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eT. harzianum\u003c/em\u003e T9 showed, at a pH of 8.5, high effectiveness in controlling \u003cem\u003eM. tecta\u003c/em\u003e, unlike the other two \u003cem\u003eTrichoderma\u003c/em\u003e strains (Figure 2A). This observation was consistent across three independent biological replicates, and the reduction in the diameter of \u003cem\u003eM. sp\u003c/em\u003e was statistically significant by Tukey-HSD test (p \u0026lt; 0.05) when competing against \u003cem\u003eT. harzianum\u003c/em\u003e T9 (Figure 2B). Notably, at this pH, \u003cem\u003eT. harzianum\u003c/em\u003e T9 was able to overgrow the pathogenic strain, with a distinct necrotic area visible after 96 and 120 hours on the backside of the petri dishes. We observed the same in \u003cem\u003eN. rosae\u003c/em\u003e at this pH (Fig. 2C). The graphs show significant degree of growth inhibition exerted by the \u003cem\u003eT. harzianum\u003c/em\u003e T9 strain compared to that of the strains \u003cem\u003eT. atroviride\u003c/em\u003e IMI 206040 and \u003cem\u003eT. harzianum\u0026nbsp;\u003c/em\u003eM10\u003cem\u003e\u0026nbsp;\u003c/em\u003e(Figure 2D)\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssembly and prediction of genome elements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo delve into the particularities of the \u003cem\u003eT. harzianum\u003c/em\u003e T9 strain and try to correlate part of its physiology with its genetic characteristics, we decided to get its genome to look for traits linked to its activity. Therefore, we sequenced, assembled and annotated the genome. First, we used the illumina reads to obtain a genome profile using tools Jellyfish version 2.2.10 [58] and GenomeScope version 1 [59] to analyze k-mer frequencies from the genome reads. This analysis (Figure 3A), allowed us to estimate a genome size of 39.85 Mb, which was consistent with the genome assembly obtained in 134 contigs using SPADES. This genome has a GC content of 48.24%, an N50 of 903,272 (Supplementary Table 2), and an estimated completeness of 99.3%. These results confirm the high quality of the assembly (Figure 3B). The genome size and GC content of the \u003cem\u003eT. harzianum\u003c/em\u003e T9 assembly were comparable to those reported for other \u003cem\u003eT. harzianum\u003c/em\u003e strains sequenced using both short- and long-read technologies [60, 61, 62]. This suggests that the primary genomic characteristics of the species remain consistent.\u003c/p\u003e\n\u003cp\u003eAdditionally, the content TEs was analyzed using the Earl Grey tool [23]. This analysis revealed the presence of various types of TEs, including DNA transposons, LINEs (Long Interspersed Nuclear Elements), LTRs (Long Terminal Repeat retrotransposons), and other repetitive elements such as simple repeats, microsatellites, and RNA (Figure 3C). LTR retrotransposons were the most prevalent type of TEs, representing 2.30% of the genome (377 elements). In contrast, the largest number of individual elements belonged to the \u0026quot;Other\u0026quot; category, which included simple repeats, RNA, and microsatellites, accounting for 9851 elements and 1.07% of the genome. DNA transposons and LINE elements were less abundant, representing 0.18% and 0.45% of the genome, respectively. Overall, TEs constituted approximately 5.56% of the genome, distributed across 2661 distinct subcategories (Supplementary Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene prediction in the assembled genome was performed using BRAKER2, resulting in 12,058 predicted genes, with an average gene size of 1,668 bp and a median gene size of 1,393 bp. The average exon size was 527 bp, and the median exon size was 284 bp (Figure 4A, C, D). These results are comparable to other \u003cem\u003eTrichoderma\u003c/em\u003e genome annotations, where gene counts and BUSCO completeness statistics generally fall within a similar range, further supporting the robustness of the annotation. The quality of the annotation was assessed using BUSCO in transcriptome mode. To accomplish this, the transcripts of the genes predicted by BRAKER2 were extracted using GFFread v0.12.7 [63] and subjected to analysis. BUSCO results showed that 100% of the predicted gene transcripts were complete, with 96% identified as single-copy genes. No fragmented or missing transcripts were detected, indicating that the structural annotation of the genome is of high quality (Figure 4B). This indicates that we have an excellent assembly, covering the largest possible number of coding regions, which provides strong support for our future functional conclusions using this genome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCAZymes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCarbohydrate-active enzymes (CAZymes) mediate the breakdown of plant cell walls, facilitating fungal interaction with the environment. In \u003cem\u003eTrichoderma\u003c/em\u003e, they play a key role in mycoparasitism and organic matter degradation. Therefore, when analyzing a new \u003cem\u003eTrichoderma\u003c/em\u003e genome, it is essential to predict how many and what types of CAZymes are encoded, as this reveals its ecological and biotechnological potential.\u003c/p\u003e\n\u003cp\u003eThe analysis of CAZymes, classified a total of 424 proteins as CAZymes, representing 3.5% of the 12,058 proteins used as input (Fig. S4A). Among these, 230 GH (glycoside hydrolases), 86 GT (glycosyltransferases), 51 AA (auxiliary activities), 20 CE (carbohydrate esterases), 8 PL (polysaccharide lyases), 1 CBM (carbohydrate-binding module), and 28 GH+CBM were identified. Out of the 424 CAZymes, 197 (46.5%) contained a signal peptide, suggesting that these proteins might be secreted (Fig. S4B; Supplementary Dataset 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePhylogenetic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo move beyond the traditional descriptions based on phylogenies inferred from single molecular markers such as ITS, we generated a phylogenomic tree using all orthologous genes shared among 30 full-length genomes from other \u003cem\u003eHypocreales\u003c/em\u003e species. Out of the 30 genomes analyzed, 28 correspond to \u003cem\u003eTrichoderma\u003c/em\u003e species, while the remaining two belong to \u003cem\u003eFusarium\u003c/em\u003e species, which were included as outgroups. All genomes underwent \u003cem\u003ede novo\u003c/em\u003e annotation using BRAKER2. Prior to the phylogenetic analysis, genome annotations and quality were assessed using BUSCO in both genome and transcriptome modes. The BUSCO results demonstrated high-quality structural and functional annotations for most of the genomes (Supplementary Figure 3).\u003c/p\u003e\n\u003cp\u003eThe phylogenomic tree inference was performed with ROADIES [51] revealing robust clustering of the \u003cem\u003eTrichoderma\u003c/em\u003e genus, supported by high bootstrap values, indicating strong evolutionary relationships among fungal species (Figure 5A). The phylogenetic tree showed well-defined clades, such as the group containing \u003cem\u003eTrichoderma hamatum\u003c/em\u003e, \u003cem\u003eTrichoderma asperellum\u003c/em\u003e, and \u003cem\u003eT. atroviride\u003c/em\u003e, which displayed notable divergence from other genus members, suggesting specific evolutionary adaptations. Additionally, \u003cem\u003eTrichoderma virens\u003c/em\u003e clustered with \u003cem\u003eTrichoderma aggressivum\u003c/em\u003e, suggesting shared phenotypic and ecological traits. The clade comprising multiple \u003cem\u003eT. harzianum\u003c/em\u003e strains included the T9 strain, which was most closely related to \u003cem\u003eT. harzianum\u003c/em\u003e Th0179, while remaining within the \u003cem\u003eharzianum\u003c/em\u003e group. This indicates a close evolutionary relationship among these strains. Our analysis clearly indicates that T9 belongs to \u003cem\u003eT. harzianum\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, we identified clusters of orthologous genes shared among \u003cem\u003eT. harzianum\u0026nbsp;\u003c/em\u003estrains (T9, \u003cem\u003eM10\u003c/em\u003e, T22, Th0179 and CBS). This analysis revealed a conserved core of 10,208 orthologous genes shared among all five strains, suggesting a highly conserved genetic basis (Figure 5B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSecondary metabolism genes show a high number of nucleotide changes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince the \u003cem\u003eT. harzianum\u003c/em\u003e T9 strain shares many orthologs with other \u003cem\u003eT. harzianum\u003c/em\u003e strains and containsrelatively few strain-specific genes (Fig. 6A), we hypothesized that most differences occur at thenucleotide level in gene variants. For this reason, we performed a variant analysis, comparing all orthologous genes between strain T9 and \u003cem\u003eT. harzianum\u003c/em\u003e strains Th6, M10, T22, Th0179, Th3844, and TR274, as well as \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 as a control due to its evolutionary distance, as shown in the phylogenetic tree in Figure 6A, where \u003cem\u003eT. atroviride\u003c/em\u003e forms a separate clade from \u003cem\u003eT. harzianum\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eOur initial SNP analysis revealed that the comparison between \u003cem\u003eT. harzianum\u003c/em\u003e\u0026nbsp; T9 and \u003cem\u003eT. atroviride\u003c/em\u003e displayed a very broad variation, indicating that almost all genes exhibit changes, an expected result since they are different species. In contrast, comparisons between \u003cem\u003eT. harzianum\u003c/em\u003e\u0026nbsp; T9 and the other \u003cem\u003eT. harzianum\u003c/em\u003e strains showed a narrower distribution, with an average of ~5% variation per gene (Fig. 6A). However, some genes appeared as outliers, showing more than 10% nucleotide changes across their coding sequences.\u003c/p\u003e\n\u003cp\u003eWe therefore focused on differences between \u003cem\u003eT. harzianum\u003c/em\u003e T9 and the\u003cem\u003e\u0026nbsp;T. harzianum\u003c/em\u003e M10 genome, a \u003cem\u003eT. harzianum\u003c/em\u003e strain that we previously used in competition assays against phytopathogenic fungi. In our initial inspection, certain genes clearly displayed a substantial number of SNPs throughout their coding regions (Fig. 6B). While some of these genes lacked functional annotation, it was noteworthy that several of the most divergent genes were involved in secondary metabolism, such as Glutathione S-transferase [64], which showed 13% variation, and a T1PKS [65], with 12% variation.\u003c/p\u003e\n\u003cp\u003eFurther annotation of the genes with the highest number of changes revealed the presence of enzymes relevant to secondary metabolite biosynthesis and modification (Fig. 6C), including a cytochrome P450, an enoyl reductase, which participates in both polyunsaturated fatty acid (PUFA) and polyketide biosynthesis in bacteria and fungi [66, 67], two short-chain dehydrogenase/reductases, typically components of large multi-domain enzymes such as mammalian fatty acid synthases or bacterial polyketide synthases [68], as well as an amidase, a beta-lactamase, an aldo/keto reductase, and an alpha/beta hydrolase.\u003c/p\u003e\n\u003cp\u003eThe high density of variants observed in the coding regions of these genes suggests a complex evolutionary scenario in which most changes may be selectively neutral, but a subset could be under positive selection, driving adaptive modifications. Given their association with secondary metabolism and the enhanced biocontrol ability of strain \u003cem\u003eT. harzianum\u003c/em\u003e T9, these variants may contribute to increased production or diversification of bioactive metabolites, potentially leading to chemical innovations that improve competitive interactions with other organisms. This finding underscores the need for an in-depth functional characterization to identify which of these changes have adaptive value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBGC Diversity and Distribution Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate whether \u003cem\u003eT. harzianum\u003c/em\u003e T9 harbors novel biosynthetic pathways for metabolites that could contribute to the control of phytopathogenic fungi, we annotated the presence of 76 BGCs in its genome, using AntiSMASH (Figure 7). We then examined whether any of the genes with high nucleotide variation rates were part of the identified BGCs. As shown in Table 1, we found 16 genes with more than 10% nucleotide variation that are located within BGCs, including gene g11550, which encodes the core enzyme of cluster 8.2, a polyketide synthase (PKS) for which no associated metabolite has yet been identified, and gene g2166, the core of cluster 14.2, a nonribosomal peptide synthetase (NRPS) potentially involved in the biosynthesis of dichlorodiaporthin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUntargeted metabolomic analysis reveals divergent peptaibols in \u003cem\u003eT. harzianum\u003c/em\u003e\u003c/strong\u003e \u003cstrong\u003eT9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven that our genomic analysis suggests that many of the differences between \u003cem\u003eT. harzianum\u003c/em\u003e T9 and other \u003cem\u003eT. harzianum\u003c/em\u003e species may be related to secondary metabolism, we sought to characterize the chemical diversity produced under the growth conditions tested in this study. To profile the metabolites synthesized by \u003cem\u003eT. harzianum\u003c/em\u003e T9, the strain was cultured on PDA plates, and the resulting metabolites were extracted for UHPLC-MS/MS analysis. The spectral data were processed using MZmine3 and submitted to Feature-Based Molecular Networking (FBMN), a workflow available through the Global Natural Products Social Molecular Networking (GNPS) platform. Compound annotations obtained from the FBMN workflow were then manually curated.\u003c/p\u003e\n\u003cp\u003eWe used BGC annotation data to corroborate the mass spectrometry-based structural annotations while assigning known or putative BGCs to most of the detected molecules. Among the annotated compounds, we found an 11-residue amino acid peptaibol (Harzianin HB I) with \u003cem\u003em/z\u003c/em\u003e of 1189.79 (Figure 8A), for which we annotated and proposed a chemical structure based on fragmentation patterns (figure 8B). MS-MS network analysis linked this ion to other less abundant molecules, including 14-residue amino acid peptaibols (Supplementary Figure 5). We proposed structures for the most abundant ions correspondent to \u003cem\u003em/z\u003c/em\u003e = 1175.7762 [M+H]\u003csup\u003e+\u003c/sup\u003e (Figure 8B), \u003cem\u003em/z\u0026nbsp;\u003c/em\u003e=1189.7918 [M+H]\u003csup\u003e+\u003c/sup\u003e (Figure 8C), and \u003cem\u003em/z\u003c/em\u003e= 1442.9344 [M+H]\u003csup\u003e+\u003c/sup\u003e (Figure 8D; Supplementary Table 4). The MS2 fragment analysis showed several shared ions, indicating that the peptaibol products share most of their amino acid sequences which suggested that they are all derived from a single Non-Ribosomal Peptide Synthetase (NRPS) involving a module skipping mechanism. Such NRPS system (Tex2) has already been reported for other peptaibols in \u003cem\u003eT. virens\u003c/em\u003e Gv29-8 [69].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Thus, \u003cem\u003eT. harzianum\u003c/em\u003e T9 must have a NRPS featuring 14 modules, an N-terminal acyl loading module and a C-terminal reductase domain. Only one NRPS with this characteristic was found in the genome of \u003cem\u003eT. harzianum\u003c/em\u003e T9, while no NRPS capable of generating 11-amino-acid peptaibols was found. A remarkable difference between \u003cem\u003eT. harzianum\u003c/em\u003e T9 NRPS and Tex2 from\u003cem\u003e\u0026nbsp;T. virens\u003c/em\u003e is that the most abundant products of Tex2 are 14-membered peptaibols while for \u003cem\u003eT. harzianum\u003c/em\u003e T9 the major product consist of 11-membered peptaibols.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate whether this BGC is present in other \u003cem\u003eTrichoderma\u003c/em\u003e species and shed light on the evolution of these peptaibols, we performed a phylogenomic analysis using our panel of \u003cem\u003eTrichoderma\u003c/em\u003e genomes. For this purpose, we used an improved version of the CORASON pipeline, optimized for fungal genomes and BGC (https://github.com/WeMakeMolecules/fungison). The analysis revealed that Tex2 from \u003cem\u003eT. virens\u003c/em\u003e and the T9 NRPS (t9_Tex2) are orthologs, and that their BGC is highly syntenic among \u003cem\u003eT. harzianum\u003c/em\u003e, \u003cem\u003eT. virens\u003c/em\u003e, \u003cem\u003eTrichoderma crassum\u003c/em\u003e, and \u003cem\u003eT. aggressivum\u003c/em\u003e (Figure 9A) while in other lineages Tex2 orthologs are lost but the rest gene neighborhood remains conserved. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also identified additional secondary metabolites produced by \u003cem\u003eT. harzianum\u003c/em\u003e T9 when grown under laboratory conditions on PDA medium, including harzianic acid (m/z = 364.1762; Fig. S6), harzianopyridone (m/z = 282.1340; Fig. S7), desferrichrocin (m/z = 718.3364; Fig. S8), pachybasin (m/z = 239.0705; Fig. S9), dimerumic acid (m/z = 485.2608; Fig. S10) and tricholignan (m/z = 221.1173; Fig. S11). Their structures were elucidated using biosynthetic gene clusters (BGCs) 19.2, 4.2, 11.1, 27.1, and 7.2 as references (Table S5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMost biocontrol agents currently used in agriculture have been isolated from agricultural soils [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], which are typically non-extreme and less challenging environments. It can be hypothesized that microorganisms from extreme environments not only develop mechanisms to resist environmental stresses, such as high temperatures, drought, UV radiation and osmotic stress, but also need to compete for the scarce nutrients available in these ecosystems. One case is that of microorganisms capable of thriving in deserts. Deserts impose multiple challenges, including physical barriers and competition with other microorganisms for limited nutrients. Therefore, these microorganisms must establish associations with plants to acquire nutrients such as photosynthates, while also effectively competing with other organisms for these resources. At the same time, they may promote plant growth and protect the host against pathogens, resulting in a commensal relationship.\u003c/p\u003e\u003cp\u003eIn the case of the genus \u003cem\u003eTrichoderma\u003c/em\u003e, it is known that these fungi can parasitize other fungi [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and the application of this mechanism is useful to control the growth of phytopathogenic fungi, which benefits agricultural crops [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. However, with increasing temperatures due to climate change, several phytopathogenic fungi have developed more efficient competitive mechanisms [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] enabling them to resist attacks from commercially available \u003cem\u003eTrichoderma\u003c/em\u003e species.\u003c/p\u003e\u003cp\u003eThe desert-derived strain \u003cem\u003eT. harzianum\u003c/em\u003e T9, isolated from the rhizosphere of \u003cem\u003eAgave lechuguilla\u003c/em\u003e in Nuevo Le\u0026oacute;n, Mexico [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], exhibited highly effective biocontrol activity against aggressive phytopathogenic fungi affecting strawberry crops in Mexico. For comparison, the performance of \u003cem\u003eT. atroviride\u003c/em\u003e IMI206040 (widely recognized for its efficacy against pathogens of the genera \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eBotrytis\u003c/em\u003e, and \u003cem\u003eAlternaria\u003c/em\u003e [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]) and \u003cem\u003eT. harzianum\u003c/em\u003e M10 was also evaluated. Both strains displayed considerably lower efficiency against strawberry-associated pathogens. These results indicate that the genetic repertoire of \u003cem\u003eT. harzianum\u003c/em\u003e T9 provides a distinct competitive advantage, enabling it to surpass even other strains of the same species and suggesting specific genetic adaptations that underlie its superior biocontrol potential.\u003c/p\u003e\u003cp\u003eTo determine the genetic characteristics responsible of these properties in strain \u003cem\u003eT. harzianum\u003c/em\u003e T9, we performed complete genome sequencing. Our phylogenomic analysis confirmed that T9 strain belongs to the \u003cem\u003eharzianum\u003c/em\u003e species, validating previous ITS-based results [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Interestingly, \u003cem\u003eT. harzianum\u003c/em\u003e T9 shares approximately 85% of orthologous genes with the \u003cem\u003eharzianum\u003c/em\u003e clade, indicating that differences between strains may stem from a small number of specific genes and amino acid point mutations.\u003c/p\u003e\u003cp\u003eBased on this, we conducted a species-wide polymorphism analysis using all high-quality \u003cem\u003eT. harzianum\u003c/em\u003e genomes available. This approach allowed us to identify genes with the highest SNP density across their ORFs. Interestingly, many of the genes with elevated polymorphism rates were involved in secondary metabolism. Such a pattern has been previously reported in fungi; for instance, in the wheat pathogen \u003cem\u003eZymoseptoria tritici\u003c/em\u003e, a study analyzing 102 isolates found that significantly associated SNPs were mostly located in coding and gene regulatory regions, with the associated genes primarily encoding transport and catalytic activities [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This trend closely mirrors our observations in \u003cem\u003eT. harzianum\u003c/em\u003e T9 when compared with other \u003cem\u003eT. harzianum\u003c/em\u003e strains, where genes related to secondary metabolite biosynthesis exhibited a high frequency of nucleotide changes. These observations led us to hypothesize that selective pressures in desert environments have driven a specialization in the secondary metabolism profile of \u003cem\u003eT. harzianum\u003c/em\u003e T9. For this reason, we set out to explore the metabolomic profile of this strain, aiming to identify the compounds it produces and to determine whether any metabolic variants could be detected.\u003c/p\u003e\u003cp\u003eThrough our metabologenomic analysis we were able to correlate several metabolites to their correspondent BGC. Interestingly, we were able to correlate seven molecules with their respective BGCs. Moreover, we observed that the most prominent peak corresponded to an 11-residue peptaibol, while a 14-residue peptaibol was also detected. Our evidence, along with a previous study by Mukherjee \u003cem\u003eet al.\u003c/em\u003e, (2011), suggests that this is synthesized by the same NRPS but with certain skipped steps, resulting in a diversity of peptaibols. These molecules were previously identified in \u003cem\u003eT. virens\u003c/em\u003e, a species highly virulent during mycoparasitism [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], suggesting that \u003cem\u003eT. harzianum\u003c/em\u003e T9 may leverage this diversity of peptaibols as an advantage during mycoparasitism. Together, these findings indicate that, in addition to enhanced stress resistance capabilities, \u003cem\u003eT. harzianum\u003c/em\u003e T9 can produce a wide variety of secondary metabolites that may contribute to its ability to inhibit the growth of phytopathogenic fungi. Moreover, we were surprised to find that, in this strain, the skipping system bypassing the synthesis of the 14-amino acid peptaibol was highly effective, leading to a significantly higher production of the 11-amino acid peptaibol. This could clearly contribute to differences in the biological activity of this compound. Further research is needed on this 11-residue peptaibol and the evolution of the skipping process in fungal NRPS.\u003c/p\u003e\u003cp\u003eThese metabolomic findings are highly relevant from an evolutionary perspective, as they suggest that certain fungi with strong biocontrol capabilities share chemical repertoires that enable them to perform this activity efficiently. However, it is particularly interesting that \u003cem\u003eT. harzianum\u003c/em\u003e T9 exhibits an increased production of an 11-amino acid compound compared to Tex2 and presumably other \u003cem\u003eT. harzianum\u003c/em\u003e strains. This may be due to modifications in transporters or even in the skipping system (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), which could contribute to its remarkable biocontrol ability by synthesizing this peptaibol in great abundance.\u003c/p\u003e\u003cp\u003eSuch modifications, or even alterations in other yet unidentified metabolites, are also likely to contribute to the observed phenotype. The shift in synthesis preference from an 11 amino acid to a 14 amino acid molecule exemplifies the type of chemical variation that may arise. Given the extensive SNP profile observed, it is reasonable to expect that multiple additional variations of this kind occur, potentially influencing the metabolic capabilities and biocontrol properties of \u003cem\u003eT. harzianum\u003c/em\u003e T9.\u003c/p\u003e\u003cp\u003eOur in-depth phylogenomic analysis of \u003cem\u003eTrichoderma\u003c/em\u003e genomes suggests that it is possible to identify the key mechanisms of competition and predict some secondary metabolic features from assembled genomes. This could represent a novel strategy to identify highly efficient fungal biocontrol agents based on their genetic characteristics. This approach is particularly relevant in the post-genomic era, where the cost of genome sequencing is relatively low, making routine genomic screening for new strains with desirable biotechnological applications feasible even before experimental validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOur study demonstrates that \u003cem\u003eT. harzianum\u003c/em\u003e T9 is a highly versatile strain, capable of thriving in extreme environmental conditions while effectively controlling common strawberry pathogens in M\u0026eacute;xico. We propose this strain as a promising biocontrol agent for crops cultivated in high-pH soils and under drought stress.\u003c/p\u003e\u003cp\u003eIn addition, our genomic analysis enabled a detailed characterization of SNP distribution across the \u003cem\u003eT. harzianum\u003c/em\u003e T9 genome, revealing specific changes within biosynthetic clusters. Notably, we observed alterations in the synthesis preferences of a peptaibol, underscoring the strain\u0026rsquo;s versatility in producing antifungal compounds. This biosynthetic pathway is also present in \u003cem\u003eT. virens\u003c/em\u003e, a species recognized for its strong antifungal capacity. By identifying such patterns, our metabologenomics approach highlights its potential for characterizing novel strains with biocontrol potential.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAdditional Information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary material is in the document SupplementaryFiguresT9.docx\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Rebollar-Alviter for the kind donation of the \u003cem\u003eN. rosae\u003c/em\u003e strain, Axel S\u0026aacute;nchez-Fonseca, and Juan Ignacio Mac\u0026iacute;as for their technical support in this work. We also thank Dr. Louise Glass at UC Berkeley for donating two model \u003cem\u003eTrichoderma\u003c/em\u003e strains, which we used for comparison against our \u003cem\u003eT. harzianum\u003c/em\u003e T9 strain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.M.V.E. and V.O.M. conceived and designed the intellectual content; F.V.G. conducted strain isolation experiments, genetic identification, and biocontrol assays. Additionally, they contributed to the manuscript writing; E.P.S. performed the sequencing experiments and genomic analyses presented in this study; A.V.G.L. performed the bioinformatic analyses for gene variant identification; A.V.G.L.,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eA.D.G.V., P.C.M., A.C.C., D.R., L.A., and E.T.A.C. contributed to data collection, analysis, and manuscript revision; J.M.V.E. and V.O.M. secured funding for the project and drafted the manuscript; All authors have read and approved the final manuscript and agree to be responsible for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe work of ACC, PCM, DR was funded by the Novo Nordisk Foundation with grant NNF20CC0035580. The antagonism tests were carried out thanks to the resources derived from the CIIC 138/2025 project of the University of Guanajuato, under the responsibility of Vianey Olmedo Monfil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genome assembly (FASTA) and annotation files for \u003cem\u003eTrichoderma harzianum\u003c/em\u003e T9 have been deposited under NCBI BioProject PRJNA1231633 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1231633). The raw sequencing reads (FASTQ) have been deposited in Zenodo (https://zenodo.org/records/16968646).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All codes used for the comparative genomics analyses are available in our GitHub repository at the following link: https://github.com/AnaValeriaG01/trichodermaT9\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSariah, M., Choo, C. W., Zakaria, H. \u0026amp; Norihan, M. S. Quantification and characterisation of \u003cem\u003eTrichoderma\u003c/em\u003e spp. from different ecosystems. \u003cem\u003eMycopathologia\u003c/em\u003e \u003cstrong\u003e159\u003c/strong\u003e, 113\u0026ndash;117 (2005).\u003c/li\u003e\n\u003cli\u003eWoo, S. L., Hermosa, R., Lorito, M. \u0026amp; Monte, E. \u003cem\u003eTrichoderma\u003c/em\u003e: a multipurpose, plant-beneficial microorganism for eco-sustainable agriculture. \u003cem\u003eNat. Rev. 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Mycoparasitism as a mechanism of \u003cem\u003eTrichoderma\u003c/em\u003e-mediated suppression of plant diseases. \u003cem\u003eFungal Biol. Rev.\u003c/em\u003e \u003cstrong\u003e38\u003c/strong\u003e, 15\u0026ndash;33 (2022). https://doi.org/10.1016/j.fbr.2021.11.004\u003c/li\u003e\n\u003cli\u003eTimmusk, S., Nevo, E., Ayele, F., Noe, S. \u0026amp; Niinemets, \u0026Uuml;. Fighting \u003cem\u003eFusarium\u003c/em\u003e pathogens in the era of climate change: a conceptual approach. \u003cem\u003ePathogens\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 419 (2020).\u003c/li\u003e\n\u003cli\u003eSingh, N. K., Tralamazza, S. M., Abraham, L. N., Glauser, G. \u0026amp; Croll, D. Genome-wide association mapping reveals genes underlying population-level metabolome diversity in a fungal crop pathogen. \u003cem\u003eBMC Biol.\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 224 (2022).\u003c/li\u003e\n\u003cli\u003eMukherjee, P. K., Horwitz, B. A. \u0026amp; Kenerley, C. M. Secondary metabolism in \u003cem\u003eTrichoderma\u003c/em\u003e \u0026ndash; a genomic perspective. \u003cem\u003eMicrobiology\u003c/em\u003e \u003cstrong\u003e158\u003c/strong\u003e, 35\u0026ndash;45 (2012).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Genes with the highest number of nucleotide changes identified within biosynthetic gene clusters (BGCs).\u003c/strong\u003e \u003cem\u003ePercentage\u003c/em\u003e indicates the proportion of nucleotide changes normalized by gene length. \u003cem\u003eBGC\u003c/em\u003e is the cluster identifier in the genome. \u003cem\u003eDescription\u003c/em\u003e is the functional annotation of the gene. \u003cem\u003efungiSMASH\u003c/em\u003e denotes the cluster family, and \u003cem\u003eCompounds\u003c/em\u003e shows the predicted metabolite synthesized by each BGC.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003egene_id\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBGC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFungiSMASH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg7952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e13.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003ehypotetical protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eterpene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHEx-tc1 terpene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eg11550\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.36\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1PKS-core\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1PKS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg7546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e37.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eBelongs to the metallo-dependent hydrolases superfamily. Peptidase M19 family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNRPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003everticillin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg3049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003ehypotetical protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNRPS-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg3053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e18.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003ehypotetical protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNRPS-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg8005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eBelongs to the short-chain dehydrogenases reductases (SDR) family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eT1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003etrichoxide\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg9221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e11.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003ehypotetical protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNRPS,T1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg7638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eCalcineurin-like phosphoesterase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eterpene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg9001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eProtein kinase domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eT1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg9011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eProtein of unknown function (DUF3645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eT1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg4667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.822\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eSerine hydrolase (FSH1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eT1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg4671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003ehypotetical protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eT1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg9073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eAcetyltransferase (GNAT) domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNRPS,T1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003efujikurin A,B,C,D\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg7205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.485\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eCarbon-nitrogen hydrolase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eNRPS,T1PKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eg2166\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10.262\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e14.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRPS-core\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNRPS,T1PKS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003edichlorodiaporthin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003eg4025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e10.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 210px;\"\u003e\n \u003cp\u003eGAL4-like Zn(II)2Cys6 (or C6 zinc) binuclear cluster DNA-binding domain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eterpene-precursor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eunidentified\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biocontrol, Extreme environments, Secondary metabolism, Peptaibols, Biosynthetic gene clusters","lastPublishedDoi":"10.21203/rs.3.rs-7456804/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7456804/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe search for sustainable agricultural solutions to reduce pesticide use is increasingly urgent, particularly under the growing pressures of climate change. Microorganisms from extreme environments offer valuable potential for biocontrol applications due to their unique adaptive traits, enabling survival under conditions such as high temperature, salinity and water scarcity. While \u003cem\u003eTrichoderma\u003c/em\u003e species are well-known biocontrol agents, many strains perform poorly in extreme soils with high salinity or alkaline pH. Here, we characterize \u003cem\u003eTrichoderma harzianum\u003c/em\u003e T9, an isolate from the alkaline desert soils of Nuevo Le\u0026oacute;n, M\u0026eacute;xico, that demonstrates exceptional resilience. \u003cem\u003eT. harzianum\u003c/em\u003e T9 displayed significantly greater biocontrol efficiency against phytopathogenic fungi from strawberry plants compared with other strains. Genome sequencing, phylogenomics and SNP-based variant analysis revealed numerous genes involved in secondary metabolism with elevated nucleotide substitution rates. Metabologenomics predicted chemical variations, primarily in peptaibols, and identified six additional compounds through biosynthetic gene cluster (BGC) prediction, likely contributing to its strong antifungal capacity. These findings position \u003cem\u003eT. harzianum\u003c/em\u003e T9 as a promising biocontrol agent for managing phytopathogens in degraded soils, offering an eco-friendly approach for sustainable agriculture. The unique genomic and metabolic traits of T9 highlight the untapped potential of microorganisms from extreme environments in advancing innovative strategies for crop protection and soil restoration.\u003c/p\u003e","manuscriptTitle":"Genomic and metabolomic insights into Trichoderma harzianum T9, a resilient biocontrol fungus from arid environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 13:32:53","doi":"10.21203/rs.3.rs-7456804/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-30T06:47:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T15:46:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T20:55:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168603267705383781382129400876799456632","date":"2025-10-16T03:13:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"171106630995780339438715041360955288301","date":"2025-10-15T18:36:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244356990290495311360674823957316929381","date":"2025-10-12T17:18:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119493857928496918397393923621529274282","date":"2025-10-11T18:35:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-10T07:32:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-22T09:14:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-01T04:00:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-27T23:09:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-27T23:04:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4545434f-f759-4872-86d2-bc212adca13f","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53975588,"name":"Biological sciences/Biotechnology"},{"id":53975589,"name":"Biological sciences/Microbiology"},{"id":53975590,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2026-01-12T16:08:13+00:00","versionOfRecord":{"articleIdentity":"rs-7456804","link":"https://doi.org/10.1038/s41598-025-33347-2","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-08 15:58:09","publishedOnDateReadable":"January 8th, 2026"},"versionCreatedAt":"2025-09-03 13:32:53","video":"","vorDoi":"10.1038/s41598-025-33347-2","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33347-2","workflowStages":[]},"version":"v1","identity":"rs-7456804","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7456804","identity":"rs-7456804","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}