Whole genome sequencing and analysis of the weed pathogen Trichoderma polysporum HZ-31

Whole genome sequencing and analysis of the weed pathogen Trichoderma polysporum HZ-31 | 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 Whole genome sequencing and analysis of the weed pathogen Trichoderma polysporum HZ-31 yushan He, haixia Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4124222/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2024 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In order to resolve the key genes for weed control by Trichoderma polysporum at the genomic level, we extracted the genomic DNA and sequenced the whole genome of T. polysporum strain HZ-31 on the Illumina Hiseq™ platform. The raw data were cleaned up using Trimmomatic and checked for quality using FastQC. The sequencing data were assembled using SPAdes, and GeneMark was used to perform gene prediction on the assembly results. The results showed that the genome size of T. polysporum HZ-31 was 39,325,746 bp, with 48% GC content, and the number of genes encoded was 11,998. A total of 148 tRNAs and 45 rRNAs were predicted. A total of 782 genes were annotated in the Carbohydrase Database, 757 genes were annotated to the Pathogen-Host Interaction Database, and 67 gene clusters were identified. In addition, 1023 genes were predicted to be signal peptide proteins. The annotation and functional analysis of the whole genome sequence of T. polymorpha HZ-31 provide a basis for the in-depth study of the molecular mechanism of its herbicidal action and more effective utilization for weed control. Biological sciences/Microbiology Biological sciences/Molecular biology whole genome T. polysporum HZ-31 weeds key genes Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Weeds are one of the major constraints on crop production, and the weed flora is diverse, consisting of a variety of perennial and annual grasses, broadleaf weeds, and sedges, which includes both parasitic and invasive weed species. Weed control is usually carried out using chemical herbicides and tillage. However, the overuse of these two control strategies poses significant challenges to agricultural production and ecology [1] . As the source of the Three Rivers, protecting the ecological environment in the Tibetan Plateau region is a top priority. This region also has special ecological characteristics such as high altitude, severe cold, strong radiation, and drought [2] , resulting in a very low environmental carrying capacity [3] , and the ecological environment is extremely fragile and vulnerable to damage. The extensive use of chemical herbicides has made the Tibetan Plateau region prone to ecological pollution, weed resistance and other problems. Reducing the amount of chemical herbicides used in response to the special ecological conditions of the region can effectively reduce the pollution of the ecological environment and increase crop yields. Microorganisms and their metabolites have herbicidal activity, and they have received more attention in the field of biocontrol research in recent years [4] . Microbial herbicides developed using microbial metabolites, especially phytopathogenic toxins, are usually safe, environmentally friendly, highly effective and have multiple target sites of action, so they can achieve green and efficient control and disrupt the development of weed resistance [5] . Xylomycetes are soil-borne filamentous fungi that are widely used as a source of biocontrol agents in agriculture [6] . They have effective antagonistic mechanisms [7] such as fungal parasitism [8] , antibiotics [9] or competition against plant pathogens [10] and nematodes [11] .. Some of the active products of M. xylostella have also been shown to exhibit herbicidal activity in recent years. Javaid et al. [12] demonstrated that fermentation filtrates of Trichoderma harzianum Rifai, T. pseudokoningii Rifai, T. reesei Simmons and T. viride Pers. have herbicidal activity against both of the wheat field weeds Phalaris minor L. and Rumex dentatus L. Yin et al. [13] demonstrated that harzianum A and B from T. breviccompacactum showed efficient weed control potential at low concentrations against Brassica chinensis , a Brassica herbaceous plant of the Cruciferae family. Moura et al .[14] demonstrated that methanolic extracts from T. spirale affected the photosynthesis of Senna occidentalis and Ipomoea grandifolia , an herbaceous plant of the genus Ipomoea , thereby exerting a toxic effect on these weeds. While searching for potential herbicide compounds, our laboratory found a strain of the weed causal agent T. arvense var. setosum at the stem base of Cirsium arvense var. setosum that showed highly efficient inhibition of weeds such as Avena fatua L., Chenopodium album L., Polygonum lapathifolium , and others. T. polysporum HZ-31 [15] , which can produce a variety of active substances such as 1,8-propanediol-o-xylene, 2,3-dihydroxypropyl propionate, and others [16] , is a kind of mycobacterial resource with high utilization value in weed biological control. At present, the whole genome sequence of T. polysporum has not been reported at home or abroad, and the use of the whole genome to mine the genes related to the synthesis of pathogenic and bioactive substances of T. polysporum has not been reported. Sequencing the genomes of weed-producing defensive fungi and using bioinformatics methods to search for the disease-causing genes and genes related to synthesis of bioactive substances or signaling pathway components of weed pathogens is an effective method to study the pathogenesis of weed pathogens. In this study, we sequenced the genome of T. polysporum , performed genome sequence analysis and gene function annotation to identify pathogenicity-related genes at the genome-wide level, searched for genes related to secondary metabolite synthesis in the databases and performed a systematic analysis. The results of these analyses provide a scientific basis for the in-depth study of the pathogenesis of T. polysporum in weeds. Material for testing 1.1 Test strains T. polysporum HZ-31 was provided by the Key Laboratory of Integrated Management of Agricultural Pests of Qinghai Province and was stored in the General Microbiology Center of China Microbial Culture Preservation Management Committee (CGNCC No.12867). Methods 2.1 Strain culture The laboratory strain preserved in the slant medium of T. polysporum HZ-31 was transferred into the PDA solid medium, with a sterile perforator with a diameter of 8 mm at the edge of the colony to hit the cake, and was inoculated on an aseptic operating table in 250 mL of sterile PDB liquid medium. Each bottle of inoculation included 5 ~ 8 particles, and was cultured at 25 ℃ on a shaking bed at 180 r/min for 5 ~ 7 d. After filtering with 3 layers of sterile gauze, the mycelium and spores were collected, washed three times with sterile water, frozen in liquid nitrogen and prepared for use. 2.2 Extraction of genomic DNA (gDNA) Genomic DNA was extracted using the Fungal Genomic DNA Extraction Kit. The integrity of the DNA was determined using 1% agarose gel electrophoresis, and the concentration and purity of the DNA were determined using Nanodrop and Qubit. 2.3 Library construction, sequencing, data quality control and assembly The whole genome DNA of T. polysporum HZ-31 was constructed and sequenced by Sangong Bioengineering (Shanghai) Co. The genomic DNA was sequenced using the Illumina II sequencing platform. After library construction, the library size was determined by 2% agarose gel electrophoresis, and the library concentration was measured by a Thermo Qubit 4.0 fluorescence quantification instrument. The raw image data files obtained by Illumina Hiseq™ were converted into raw sequenced reads by CASAVA Base Calling analysis. The raw data quality values and other information were determined, and the quality of the sequencing data of the samples was evaluated visually using FastQC. The raw data were filtered using Trimmomatic, which included removing the following: sequences with N bases; splice sequences in reads; low-quality bases (Q-value < 20) starting from the 3' to 5' direction of reads; low-quality bases (Q-value < 20) starting from the 5' to 3' direction of reads; bases with quality values below 20 in the tails of the reads using the sliding window method (window size of 5 bp); and the reads themselves along with their paired reads with a length of less than 35 nt. The second-generation sequencing data were spliced using SPAdes, which first corrects the sequence errors of the original sequence, then assembles it by multiple Kmer values, and finally synthesizes the assembly results of each Kmer value to obtain the best results. Then GapFiller was used to complement GAP on the contig obtained from splicing, and finally PrInSeS-G was used for sequence correction to correct the editing errors and the insertion-deletions of small fragments during splicing. 2.4 Gene element prediction GeneMark was used for gene prediction of the assembly results, with tRNAscan-SE for tRNA, RNAmmer for rRNA, and Rfam for snRNA, while RepeatModeler was used for the Denovo prediction of repetitive sequences of the assembly results. RepeatMasker was then used to find the position and frequency of occurrence of each type of repetitive sequences on the genomic segments. 2.5 Gene function annotation The protein sequences of the predicted genes were aligned with the NR, SwissProt, TrEMBL, COG, PFAM ( http://pfam.xfam.org/ ), and CDD ( https://www.ncbi.nlm.nih.gov/cdd/ ) databases to obtain protein functional annotation using NCBI BLAST + V2.2.28 software. GO (Gene Ontology) functional annotations were obtained using the SwissProt and TrEMBL databases, and KEGG (Kyoto Encyclopedia of Genes and Genomes) annotations were obtained using KAAS V2.21 software. 2.6 Analysis of disease-causing and secondary metabolite-related genes The gene set protein sequences were aligned to the CAZy database using HMMER3 to obtain their corresponding carbohydrate-active enzyme annotation information. The screening condition was E-value < 1e-5. The gene protein sequences were aligned with the PHI-base database using BLAST to combine the annotation information of the genes and their corresponding pathogenic host interactions to obtain the final annotation results. Secondary metabolite synthesis gene clusters in strain PA-2 were predicted using the antiSMASH 3.0 online tool ( https://fungismash.secondarymetabolites.org ). SignalP software was used to predict the possible signal peptides of T. polysporum HZ-31. Results and analysis 3.1 Genome assembly Genomic DNA was extracted from T. polysporum HZ-31 strain, and the genome of T. polysporum HZ-31 was assembled using the Illumina Hiseq™ platform for the sequencing (Table 1 ). The total base length of all contigs of T. polysporum HZ-31 was 39,325,746 bp, with an average GC content of 48%, including 891 contigs with a total average length of 44,136.64 bp, and an N50 value of 155,640. The results of the genome sequencing of T. polysporum HZ-31 were uploaded to NCBI, with accession number PRJNA941260. Table 1 Assembly results of Trichoderma polysporum HZ-31 Essential feature HZ-31 Total length 39,325,746 N num 868 Max len 750,229 Average len 44,136.64 Contig num 891 N50 155,640 GC Ratio 0.48 3.2 Gene element predictions 3.2.1 Coding gene predictions GeneMark was used to predict the genes of the assembly results, and the results are shown in Table 2 . A total of 11,998 genes were predicted in the genome of T. polysporum HZ-31, and the total length of the genes was 17,908,516, which accounted for 45.54% of the length of the whole genome of T. polysporum HZ-31. The distribution map of gene lengths showed that the number of genes with lengths in the range of 800-1,000 bp was the largest, including 1,470 genes, and the number of genes with lengths of 0-200 bp was the smallest, including 84 genes(Fig. 1 B). The distribution plot of GC contents of the genes showed that the GC content ranged from 45–55%(Fig. 1 A), indicating that there was no significant bias in GC content. Table 2 Statistics of coding gene prediction results All_num >=500bp >=1000bp N50 Max_len Min_len All_len Mean_len Gene 11,998 10,619 7,423 1,809 69,120 121 17,908,516 1,492.63 3.2.2 Repeat sequence prediction The results of genome repeat sequence prediction of the sequenced strains are shown in Table 3 . The results showed that T. polysporum HZ-31 contained 14,297 repeat sequences, with a total length of 1,149,690 bp, accounting for 2.94% of the total genome length. These sequences had an average length of 80.41 bp, of which 124 were DNA, 290 were LINE, 366 were LTR, 13 were SINE and 1,610 were unknown. Table 3 Repeat sequence statistics Repeat family Regional count Base count Average length Percentage in genome DNA 124 42407 341.99 0.11% LINE 290 115631 398.73 0.30% LTR 366 120692 329.76 0.31% Low_complexity 1655 78113 47.2 0.20% SINE 13 1073 82.54 0.00% Satellite 117 12220 104.44 0.03% Simple_repeat 10122 393372 38.86 1.01% Unknown 1610 386182 239.86 0.99% All_RepeatSeq 14297 1149690 80.41 2.94% 3.2.3 Non-coding RNA predictions Non-coding RNAs are RNAs that do not code for proteins. Different strategies were used to predict the different non-coding RNAs with respect to their structural characteristics. An analysis of the results of the genomic data of T. polysporum HZ-31 shows that there are 148 transporter RNAs (tRNAs) and 45 ribosomal RNAs (rRNAs). 3.3 Gene function annotation The predicted protein sequences of the genes were compared with the functional databases, and the annotation results of gene functional analysis are shown in Table 4 . The numbers of annotated genes and the corresponding databases were: CDD 7,418, KOG 5,673, NR 11,541, PFAM 5,818, SwissProt 7,822, TrEMBL 11,531, GO 7,983, KEGG 3,841. Table 4 Gene function analysis annotated results Database Number of unigenes Percentage (%) Annotated in CDD 7,418 61.83 Annotated in KOG 5,673 47.28 Annotated in NR 11,541 96.19 Annotated in PFAM 5,818 48.49 Annotated in SwissProt 7,822 65.19 Annotated in TrEMBL 11,531 96.11 Annotated in GO 7,983 66.54 Annotated in KEGG 3,841 32.01 Total unigenes 11,998 100 3.3.1 NR annotation results Comparing the genomic genes of T. polysporum HZ-31 with the NR database(Fig. 2 ), a total of 10,360 genes were annotated to the genus Trichoderma . They accounted for 86.35% of the genome, indicating that strain HZ-31 indeed belongs to the genus Trichoderma . Among them, the most genes were annotated to Trichoderma gamsii with 3,229, followed by Trichoderma atroviride with 2,771. 3.3.2 KOG Functional classification annotation results The genomic genes of T. polysporum HZ-31 were annotated to the KOG database(Table 5 and Fig. 3 ). The metabolic pathway with the highest number of annotated genes was General function prediction with 994; followed by Posttranslational modification, protein turnover, chaperones 460; Signal transduction mechanisms 338; and finally Secondary metabolites biosynthesis, transport and catabolism 333. Table 5 KOG functional classification statistics KOG functional classification Gene_num Gene_ratio Processing and modification 214 3.77 Chromatin structure and dynamics 46 0.81 Energy production and conversion 286 5.04 Cell cycle control, cell division, chromosome partitioning 108 1.9 Amino acid transport and metabolism 324 5.71 Nucleotide transport and metabolism 73 1.29 Carbohydrate transport and metabolism 295 5.2 Coenzyme transport and metabolism 83 1.46 Lipid transport and metabolism 323 5.69 Translation, ribosomal structure and biogenesis 315 5.55 Transcription 239 4.21 Replication, recombination and repair 191 3.37 Cell wall/membrane/envelope biogenesis 167 2.94 Cell motility 3 0.05 Posttranslational modification, protein turnover, chaperones 460 8.11 Inorganic ion transport and metabolism 108 1.9 Secondary metabolites biosynthesis, transport and catabolism 333 5.87 General function prediction only 994 17.52 Function unknown 316 5.57 Signal transduction mechanisms 338 5.96 Intracellular trafficking, secretion, and vesicular transport 264 4.65 Defense mechanisms 62 1.09 Extracellular structures 6 0.11 Unnamed protein 1 0.02 Nuclear structure 5 0.09 Cytoskeleton 119 2.1 3.3.3 GO functional classification annotation results The predicted genes were categorized into cellular component, molecular function and biological process according to their functions in the GO database. The statistical results of gene functions and numbers of genes of T. polysporum HZ-31 annotated in the GO database are shown in Table 6 and Fig. 4 . There were 25,064 genes belonging to cellular components, 19 categories; 10,570 genes belonging to molecular function, 16 categories; and 24,740 genes belonging to biological process, 23 categories. Among them, the most annotated genes in cellular components are cell, with 5,713 genes; the most annotated genes in molecular functions are catalytic activity, with 4,429 genes; and the most annotated genes in biological processes are cellular processes, with 5,395 genes. Table 6 GO functional classification statistics Ontology Term Gene_num Ratio Biological process Reproduction 350 2.92 Cell killing 6 0.05 Immune system process 53 0.44 Metabolic process 4,789 39.91 Cellular process 5,395 44.97 Reproductive process 143 1.19 Biological adhesion 43 0.36 Signaling 447 3.73 Multicellular organismal process 287 2.39 Developmental process 451 3.76 Growth 118 0.98 Locomotion 70 0.58 Single-organism process 3,027 25.23 Rhythmic process 14 0.12 Positive regulation of biological process 360 3 Negative regulation of biological process 364 3.03 Regulation of biological process 1,511 12.59 Response to stimulus 1,492 12.44 Localization 1,290 10.75 Establishment of localization 1,188 9.9 Multi-organism process 246 2.05 Biological regulation 1,701 14.18 Cellular component organization or biogenesis 1,395 11.63 Cellular component Extracellular region 330 2.75 Cell 5,713 47.62 Nucleoid 18 0.15 Membrane 2,222 18.52 Virion 6 0.05 Cell junction 38 0.32 Extracellular matrix 14 0.12 Membrane-enclosed lumen 815 6.79 Macromolecular complex 1,411 11.76 Organelle 4,475 37.3 Extracellular matrix part 8 0.07 Extracellular region part 40 0.33 Organelle part 2,370 19.75 Virion part 1 0.01 Membrane part 1,822 15.19 Synapse part 33 0.28 Cell part 5,710 47.59 Synapse 36 0.3 Symplast 2 0.02 Molecular function Protein binding transcription factor activity 78 0.65 Nucleic acid binding transcription factor activity 400 3.33 Catalytic activity 4,429 36.91 Receptor activity 29 0.24 Structural molecule activity 209 1.74 Transporter activity 692 5.77 Binding 4,307 35.9 Electron carrier activity 129 1.08 Antioxidant activity 54 0.45 Channel regulator activity 3 0.03 Metallochaperone activity 7 0.06 Enzyme regulator activity 162 1.35 Protein tag 1 0.01 Translation regulator activity 5 0.04 Nutrient reservoir activity 5 0.04 Molecular transducer activity 60 0.5 3.3.4 KEGG functional classification annotation results The genes of the T. polysporum HZ-31 genome were annotated to the KEGG database into six major categories(Table 7 and Fig. 5 ), including Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases (HDP), Metabolism, and Organismal Systems, and they included 1083, 567, 1275, 1306, 4162, 1045 genes, respectively. Among these six categories, Metabolic processes had the most genes annotated, with Amino acid metabolism annotated to 836 genes; Carbohydrate metabolism annotated to 676 genes; Overview annotated to 570 genes; Lipid metabolism annotated to 459 genes; and Xenobiotics biodegradation and metabolism annotated to 432 genes. Table 7 KEGG functional classification statistics Type Subgroup Gene_num Organismal Systems Nervous system 228 Excretory system 52 Sensory system 68 Circulatory system 71 Immune system 195 Endocrine system 275 Environmental adaptation 40 Digestive system 85 Development 31 Metabolism Metabolism of terpenoids and polyketides 42 Energy metabolism 263 Nucleotide metabolism 242 Carbohydrate metabolism 676 Glycan biosynthesis and metabolism 131 Lipid metabolism 459 Xenobiotics biodegradation and metabolism 432 Overview 570 Metabolism of cofactors and vitamins 276 Amino acid metabolism 836 Metabolism of other amino acids 159 Biosynthesis of other secondary metabolites 76 Human Diseases Endocrine and metabolic diseases 64 Cardiovascular diseases 29 Immune diseases 27 Infectious diseases 544 Drug resistance 4 Cancers 357 Neurodegenerative diseases 217 Substance dependence 64 Genetic Information Processing Folding, sorting and degradation 371 Replication and repair 230 Translation 451 Transcription 223 Environmental Information Processing Membrane transport 50 Signal transduction 517 Cellular Processes Cell growth and death 673 Cell communication 104 Transport and catabolism 268 Cell motility 38 3.4 Analysis of secondary metabolite-related genes 3.4.1 Carbohydrate-active enzymes (CAZymes) Phytopathogenic fungi secrete a variety of carbohydrate-active enzymes, which are subdivided into different families according to their functions, such as Glycoside Hydrolases (GHs), Glycosyl Transferases (GTs), Polysaccharide Lyases (PLs), and Carbohydrate Esterases (CEs), Auxiliary Activities (AAs), and Carbohydrate-Binding Modules (CBMs) [ 17 ] . The protein encoding genes for T. polysporum HZ-31 were annotated to the CAZy database with a total of 782 genes(Fig. 6 ), the largest number of which were annotated to the Glycoside hydrolase family with 296 (37.85%); the smallest number of genes were annotated to the Polysaccharide cleavage enzyme family with 11 (1.41%). The remaining genes annotated to glycosyltransferases, sugar esterolytic enzymes, oxidoreductases, and carbohydrate-binding structural domains numbered 155, 139, 116, and 65 genes, respectively, for percentages of 19.82%, 17.77%, 14.83%, and 8.31%, respectively. The most genes annotated to the T. polysporum HZ-31GH family were the genes encoding GH18 with 35, followed by GH5, GH16, and GH3 with 21, 19, and 19, respectively. The most frequently annotated gene in the HZ-31GT family of T. polysporum was the genes encoding GT41, with 50 genes, followed by GT32, GT2, and GT21, with 12, 11, and 8 genes, respectively. 3.4.2 Secondary metabolic gene clusters Secondary metabolites are key factors in the phytotoxic activity of pathogenic fungi, and a variety of phytotoxic secondary metabolites, including polyketides, non-ribosomal peptides, terpenes, and alkaloids, are used to kill host cells. A total of 67 gene clusters were identified in the genome of T. polysporum HZ-31(Fig. 7 ). The highest percentages were in the polyketide synthase gene clusters of type I (T1PKS), non-ribosomal peptide synthase-like gene clusters (NRPS-Like), peptide-like clusters synthesized and post-translationally modified in the fungal ribosome (fungal-RiPP-like), and non-ribosomal peptides (NRPS), and the highest percentage of terpenes (TERPENE), while NRP-metallophore accounted for less. BLAST comparison of all gene clusters of T. polysporum HZ-31 with known secondary metabolite gene clusters revealed that the 1421_g gene in the NRPS-Like, fungal-RiPP-like gene cluster type encoded material that was 100% similar to the choline gene cluster, while the 2916_g and 2919_g genes in the NRPS,T1PKS gene cluster type encoded material that was 66% similar to C. albicans beauvericin. The similarity of the substances encoded by the 2916_g and 2919_g genes to beauvericin was 66%, while the 5536_g genes in the NRPS cluster type showed 100% similarity to verticillin. The 6364_g genes in the NRPS cluster type showed 100% similarity to the peramine/intermediate 1/intermediate 2 genes. The similarity of the 7085_g in the NRPS cluster type to enniatin was 100%, and the similarity of 7561_g in the NRPS cluster type to (-)-Mellein was 100%. The 7905_g gene encoded a substance which showed 100% similarity to trichoxide. The substance encoded by the 4736_g gene in the NRPS-like,T1PKS gene cluster type showed 50% similarity to swainsonine. 3.4.3 PHI pathogenicity related genes PHI annotation of the genomic genes of T. polysporum HZ-31 showed that a total of 757 genes were annotated in the database for pathogen-host interactions(Fig. 8 ). When the pathogen genes were functionally categorized, the highest number of genes were annotated as reduced virulence, 370; followed by unaffected pathogenicity, 244; loss of pathogenicity, 82; lethal, 30; and resistance, 30; chemistry target: resistance to chemical, 15; and effector: plant avirulence determinant, 117, A total of 47 were annotated as increased virulence, hypervirulence. Among them, the GPA1 gene, with a relatively high number of annotations in the Pathogen-Host Interaction Database (PHIDB), was numbered gene9200 in the genome of strain HZ-31, with a total length of 1,062 bp. It encodes the G protein α subunit, which is related to the nutrient growth, sporulation, adherent cell formation, and toxin production of the fungus, and is involved in the pathogenicity of Cryptoccus neo formans, Aspergillus nidulans , Ustilago esculenta , Fusarium graminearum and T.harzianum. The knockdown of the GPA1 gene could cause the complete loss of pathogenicity of F. graminearum GPA1 mutant on wheat spikes [ 18 ] . Further studies have also confirmed that the GPA1 gene can affect adherent cell formation and the expression of several virulence-related genes associated with infestation through the regulation of intracellular cAMP levels. 3.4.4 Signaling peptide proteins A total of 1,023 signal peptide proteins (8.53%) were predicted for T. polysporum HZ-31(Table 8 ). Table 8 Signal peptide protein prediction statistics Organism type Total protein number Signal proteins from SignalP-TM Signal proteins from SignalP-noTM Total sigan proteins Signal protein ratio (%) euk 11998 30 993 1023 8.526421 Discussion Trichoderma polysporum HZ-31 is a microbial fungus with great potential for weed control, and it is difficult to comprehensively analyze the mechanism of action of T. polysporum by traditional experimental and identification methods. In this regard, an in-depth study of the intrinsic causes of T. polysporum pathogenicity at the genomic level is of great significance. Therefore, we obtained the genome size of T. polysporum HZ-31 by whole genome sequencing and bioinformatics analysis as 39,325,746 bp, with 48% GC content, and the number of coding genes was 11,998. Among these genes, 148 tRNAs and 45 rRNAs were predicted in the annotated GO, COG and KEGG databases as related to amino acid metabolism, carbohydrate metabolism and lipid metabolism. A variety of carbohydrate-active enzymes secreted by plant pathogens are involved in the degradation of host plant cell walls. Several studies have shown that pathogens from animals and plants utilize carbohydrases and other nutrients to regulate their virulence and adjust their metabolism for successful colonization [ 19 ] . A total of 782 genes were annotated in the carbohydrase database in this study. Members of the glycoside hydrolase family act as virulence factors and modulate plant immune responses during pathogen infection [ 20 ] . Among them, the gene encoding GH3 was annotated in T. polysporum HZ-31, and it was found to encode a β-glucosidase that plays an important role and is a key enzyme in cellulose degradation, which is closely related to the pathogen's infectious characteristics. Wang et al. [ 21 ] found that the cell wall degrading enzyme of Ziziphus jujuba melanogaster , which plays a key role in the pathogenic process, is β-glucosidase, and its activity is the highest in the diseased-healthy junction in the process of infection. Li [ 22 ] found that there were 16 GH3 gene family members in the genome of Aspergillus sphaericus , and the transcripts of most of them were up-regulated under the induction of cellulose, which was consistent with the changes in extracellular β-glucosidase activity. Studies have suggested that the GH3 gene family in Xylaria plays an important role in cellulose degradation and plant pathogenicity. Glycosylation is an important post-translational modification of proteins, which can affect the solubility, stability and catalytic activity of proteins, and also has important biological functions related to protein folding, localization and translocation. In recent years, a growing number of studies have demonstrated that glycosyltransferases are closely related to pathogenic virulence and play key roles in biological processes such as the adhesion, immune escape and colonization of pathogenic bacteria. The gene encoding GT2 of the glycosyltransferase family in T. polysporum HZ-31 was annotated, and GT2 was shown to be involved not only in biomass synthesis, but also in many complex aspects of cellular processes in fungi. Zhang et al. [ 23 ] used CRISPR/Cas9 and homologous recombination techniques involving deletion and backfilling of the PaGt2 gene of the GT2 family encoding glycosyltransferase, and found that the strain was significantly inhibited in nutrient growth, did not produce conidiophores and conidia, and had significantly reduced pathogenicity on peach shoots and fruits. Genomics, molecular biological and bioinformatics studies have shown that the genes encoding enzymes which produce various fungal secondary metabolites are clustered and often in close proximity to telomeres [ 24 ] . The genes that are found in clusters of secondary metabolite synthesis genes are frequently co-regulated according to the functions of the secondary metabolites encoded by these genes [ 25 ] . Furthermore, an increasing number of secondary metabolite synthesis genes are thought to be closely related to, or even regulate, the pathogenicity of pathogenic bacteria. In this study, the T. polysporum HZ-31 secondary metabolite synthesis gene cluster was annotated to genes that synthesize toxins such as enniatin, beauvericin and Mellein. Beauvericin is a non-specific phytotoxin with toxic effects on many cell lines, and the essential components in its synthesis are the amino acids L-Phe, D-HYIV, ATP/Mg + and ADOMet [ 26 ] . The mechanism of its cytotoxicity involves its role as a K + ion carrier, in which it can be embedded in biological membranes, forming channels, triggering the elevation of Ca 2+ in the cytoplasm, affecting the electrochemical gradient of the cell membrane, and ultimately inducing a series of cytotoxic reactions. Beauvericin can also enter the nucleus of plant cells, combine with DNA to form DNA-BEA complexes, and through calcium-dependent endonuclease cleavage of the bound DNA, it can interrupt chromosomes and cause toxicity. Chen [ 27 ] found that knockdown of the leukocidin homologous gene FOXB_16250 in Fusarium spinosum Fo5176 resulted in a reduction in the pathogenicity of the Fo5176 mutant, as well as a delay in the onset of disease in the mutant inoculated into Columbia-type Arabidopsis thaliana wild-type plants, suggesting that leukocidin synthesis genes inhibit Fusarium spinosum pathogenicity. Enniatin is a hexapeptide fungal toxin that is present in the mycelium and can have a strong toxic effect on the cellular tissues of plants [28]. The esyn1 gene is an important regulator of the biosynthesis of enniatin. Chen et al. [ 29 ] cloned the esyn1 gene from Foc4, and compared with the wild strain, the biosynthesis of Fusarium enantiospirillum was significantly reduced in the knockout mutant. Furthermore, the pathogenicity of the mutant was completely lost, whereas backfilling of the wild strain was able to restore the biosynthesis and pathogenicity of Fusarium enantiospirillum , which suggests that this gene is a key factor in the pathogenic bacterial infections of the plant. Mellein is a known compound with various phytotoxic, cytotoxic, fungicidal, antimicrobial and larvicidal activities reported [ 30 ] . Li et al. [ 31 ] demonstrated the presence of (R)-(-)-mellein in the fermentation broth of Vitis vinifera and found that it was also present in Vitis vinifera -infected apple fruits and twigs, and that there was a relationship between lesion expansion and honey curdling mycorrhizal fungal pigmentation in the apple tissues. Phytotoxicity bioassays have shown that honeystrobin causes discoloration and death of apple leaves and browning of stems. Another study showed that the main components of the toxin of Sphaeropsis sapinea , a pine tree dieback disease, are also the above two forms of R-(-)-Mellein and 4-hydroxyMellein, in which R-(-)-Mellein plays a major role, while the other two are synergistic with each other in the toxin and antifungal activity assays [ 32 ] . These secondary metabolites play a key role in the pathogenicity of pathogens in their host plants, and the gene clusters that regulate the synthesis of secondary metabolites are fundamental in the regulation of that pathogenicity. The gene clusters of secondary metabolite synthesis annotated in the present study may also play important roles in the pathogenicity of T. polysporum HZ-31 in weeds. By sequencing the whole genome of the T. polysporum HZ-31 strain, all the genetic information for the genome of this pathogen was obtained. Many virulence-related pathogenic genes were found, which were mainly involved in cell wall catabolic enzymes, strain nutrient growth and biomass synthesis, etc. The genome information also showed that T. polysporum HZ-31 contains a large number of genes involved in toxin biosynthesis, suggesting that T. polysporum HZ-31 is able to produce a variety of toxins during the infestation process. The present study bridges the gap in the genomic information of this strain, and also provides the necessary genetic background information for further analyzing the herbicidal mechanism of this strain. Declarations Conflict of interest The authors declare no conflict of interest. Contributions Yushan He and Haixia Zhu conceived the study, collected samples, and did lab steps. Yushan He analysed the results and wrote the manuscript. Author Contribution H. and Z. conceived the study, collected samples, and did lab steps. H. analysed the results and wrote the manuscript. Acknowledgements The research was funded by basic research project (2024-ZJ-928) of Science and Technology Department of Qinghai Provincethe. We are also grateful to the anonymous reviewers for their valuable suggestions and comments. Data Availability The sequence data supporting the results of this study has been stored in the NCBI with the main entry code PRJNA941260 References Menalled, U.D., Smith, R.G., Cordeau, S. et al. Phylogenetic relatedness can influence cover crop-based weed suppression. Sci Rep 13, 17323 (2023). https://doi.org/10.1038/s41598-023-43987-x Song ,W.M. Weed community composition and its prevention and control countermeasures in Qinghai oat field[D]. 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The DOI: 10.27327 /, dc nki. Gshnu. 2020.000606. De Felice, B, Spicer, L.J, Caloni, F. Enniatin, B.1: Emerging Mycotoxin and Emerging Issues. Toxins (Basel). 2023;15(6):383. doi: 10.3390/toxins15060383 . PMID: 37368684; PMCID: PMC10303499. Chen,S, Li, C.Y, Yi, G.J et al. Cloning and sequence analysis of esyn1 gene from Fusarium fusarium banana [J]. Journal of Tropical Crops,2011,32(08):1503–1506. Khambhati ,V.H, Abbas, H.K, Sulyok, M, Tomaso-Peterson M, Chen J, Shier WT. Mellein: Production in culture by Macrophomina phaseolina isolates from soybean plants exhibiting symptoms of charcoal rot and its role in pathology. Front Plant Sci. 2023;14:1105590. doi: 10.3389/fpls.2023.1105590 . PMID: 36844080; PMCID: PMC9944435. Li .Y, G,i Z, Wang, C, Li, P, Li ,B. Identification of Mellein as a Pathogenic Substance of Botryosphaeria dothidea by UPLC-MS/MS Analysis and Phytotoxic Bioassay. J Agric Food Chem. 2021;69(30):8471–8481. doi: 10.1021/acs.jafc.1c03249 . Epub 2021 Jul 24. PMID: 34304561. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4124222","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":287399184,"identity":"9c90d83c-ee66-4992-adb7-a32f7ce50641","order_by":0,"name":"yushan He","email":"","orcid":"","institution":"Qinghai University","correspondingAuthor":false,"prefix":"","firstName":"yushan","middleName":"","lastName":"He","suffix":""},{"id":287399185,"identity":"0c68611a-a685-4060-a2d1-dead8ded8036","order_by":1,"name":"haixia 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00:31:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1905391,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4124222/v1/fa42a48b-d459-4a81-9828-9a177573119a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Whole genome sequencing and analysis of the weed pathogen Trichoderma polysporum HZ-31","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWeeds are one of the major constraints on crop production, and the weed flora is diverse, consisting of a variety of perennial and annual grasses, broadleaf weeds, and sedges, which includes both parasitic and invasive weed species. Weed control is usually carried out using chemical herbicides and tillage. However, the overuse of these two control strategies poses significant challenges to agricultural production and ecology \u003csup\u003e[1]\u003c/sup\u003e. As the source of the Three Rivers, protecting the ecological environment in the Tibetan Plateau region is a top priority. This region also has special ecological characteristics such as high altitude, severe cold, strong radiation, and drought \u003csup\u003e[2]\u003c/sup\u003e, resulting in a very low environmental carrying capacity\u003csup\u003e\u0026nbsp;[3]\u003c/sup\u003e, and the ecological environment is extremely fragile and vulnerable to damage. The extensive use of chemical herbicides has made the Tibetan Plateau region prone to ecological pollution, weed resistance and other problems. Reducing the amount of chemical herbicides used in response to the special ecological conditions of the region can effectively reduce the pollution of the ecological environment and increase crop yields. Microorganisms and their metabolites have herbicidal activity, and they have received more attention in the field of biocontrol research in recent years \u003csup\u003e[4]\u003c/sup\u003e. Microbial herbicides developed using microbial metabolites, especially phytopathogenic toxins, are usually safe, environmentally friendly, highly effective and have multiple target sites of action, so they can achieve green and efficient control and disrupt the development of weed resistance \u003csup\u003e[5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eXylomycetes are soil-borne filamentous fungi that are widely used as a source of biocontrol agents in agriculture \u003csup\u003e[6]\u003c/sup\u003e. They have effective antagonistic mechanisms \u003csup\u003e[7]\u003c/sup\u003e such as fungal parasitism \u003csup\u003e[8]\u003c/sup\u003e, antibiotics \u003csup\u003e[9]\u003c/sup\u003e or competition against plant pathogens \u003csup\u003e[10]\u003c/sup\u003e and nematodes \u003csup\u003e[11]\u003c/sup\u003e.. Some of the active products of \u003cem\u003eM. xylostella\u003c/em\u003e have also been shown to exhibit herbicidal activity in recent years. Javaid et al. \u003csup\u003e[12]\u003c/sup\u003edemonstrated that fermentation filtrates of \u003cem\u003eTrichoderma harzianum\u0026nbsp;\u003c/em\u003eRifai, \u003cem\u003eT. pseudokoningii\u0026nbsp;\u003c/em\u003eRifai, \u003cem\u003eT. reesei\u0026nbsp;\u003c/em\u003eSimmons\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eT. viride\u0026nbsp;\u003c/em\u003ePers.\u003cem\u003e\u0026nbsp;\u003c/em\u003ehave herbicidal activity against both of the wheat field weeds \u003cem\u003ePhalaris minor\u0026nbsp;\u003c/em\u003eL. and \u003cem\u003eRumex dentatus\u0026nbsp;\u003c/em\u003eL. Yin et al. \u003csup\u003e[13]\u003c/sup\u003edemonstrated that harzianum A and B from\u003cem\u003e\u0026nbsp;T. breviccompacactum\u0026nbsp;\u003c/em\u003eshowed efficient weed control potential at low concentrations against \u003cem\u003eBrassica chinensis\u003c/em\u003e, a \u003cem\u003eBrassica\u003c/em\u003e herbaceous plant of the Cruciferae family. Moura et al\u003csup\u003e.[14]\u003c/sup\u003e demonstrated that methanolic extracts from\u003cem\u003e\u0026nbsp;T. spirale\u0026nbsp;\u003c/em\u003eaffected the photosynthesis of\u003cem\u003e\u0026nbsp;Senna occidentalis\u003c/em\u003e and \u003cem\u003eIpomoea\u003c/em\u003e\u003cem\u003e\u0026nbsp;grandifolia\u003c/em\u003e, an herbaceous plant of the genus \u003cem\u003eIpomoea\u003c/em\u003e, thereby exerting a toxic effect on these weeds. While searching for potential herbicide compounds, our laboratory found a strain of the weed causal agent \u003cem\u003eT. arvense\u0026nbsp;\u003c/em\u003evar.\u003cem\u003e\u0026nbsp;\u003c/em\u003esetosum at the stem base of \u003cem\u003eCirsium arvense\u0026nbsp;\u003c/em\u003evar.\u003cem\u003e\u0026nbsp;\u003c/em\u003esetosum that showed highly efficient inhibition of weeds such as\u003cem\u003e\u0026nbsp;Avena fatua\u0026nbsp;\u003c/em\u003eL.,\u003cem\u003e\u0026nbsp;Chenopodium album\u0026nbsp;\u003c/em\u003eL.,\u003cem\u003e\u0026nbsp;Polygonum lapathifolium\u003c/em\u003e, and others. \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 \u003csup\u003e[15]\u003c/sup\u003e, which can produce a variety of active substances such as 1,8-propanediol-o-xylene, 2,3-dihydroxypropyl propionate, and others \u003csup\u003e[16]\u003c/sup\u003e, is a kind of mycobacterial resource with high utilization value in weed biological control. At present, the whole genome sequence of \u003cem\u003eT. polysporum\u003c/em\u003e has not been reported at home or abroad, and the use of the whole genome to mine the genes related to the synthesis of pathogenic and bioactive substances of \u003cem\u003eT. polysporum\u003c/em\u003e has not been reported.\u003c/p\u003e\n\u003cp\u003eSequencing the genomes of weed-producing defensive fungi and using bioinformatics methods to search for the disease-causing genes and genes related to synthesis of bioactive substances or signaling pathway components of weed pathogens is an effective method to study the pathogenesis of weed pathogens. In this study, we sequenced the genome of \u003cem\u003eT. polysporum\u003c/em\u003e, performed genome sequence analysis and gene function annotation to identify pathogenicity-related genes at the genome-wide level, searched for genes related to secondary metabolite synthesis in the databases and performed a systematic analysis. The results of these analyses provide a scientific basis for the in-depth study of the pathogenesis of \u003cem\u003eT. polysporum\u003c/em\u003e in weeds.\u003c/p\u003e"},{"header":"Material for testing","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Test strains\u003c/h2\u003e \u003cp\u003e \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 was provided by the Key Laboratory of Integrated Management of Agricultural Pests of Qinghai Province and was stored in the General Microbiology Center of China Microbial Culture Preservation Management Committee (CGNCC No.12867).\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Strain culture\u003c/h2\u003e \u003cp\u003eThe laboratory strain preserved in the slant medium of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 was transferred into the PDA solid medium, with a sterile perforator with a diameter of 8 mm at the edge of the colony to hit the cake, and was inoculated on an aseptic operating table in 250 mL of sterile PDB liquid medium. Each bottle of inoculation included 5\u0026thinsp;~\u0026thinsp;8 particles, and was cultured at 25 ℃ on a shaking bed at 180 r/min for 5\u0026thinsp;~\u0026thinsp;7 d. After filtering with 3 layers of sterile gauze, the mycelium and spores were collected, washed three times with sterile water, frozen in liquid nitrogen and prepared for use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Extraction of genomic DNA (gDNA)\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted using the Fungal Genomic DNA Extraction Kit. The integrity of the DNA was determined using 1% agarose gel electrophoresis, and the concentration and purity of the DNA were determined using Nanodrop and Qubit.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Library construction, sequencing, data quality control and assembly\u003c/h2\u003e \u003cp\u003eThe whole genome DNA of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 was constructed and sequenced by Sangong Bioengineering (Shanghai) Co. The genomic DNA was sequenced using the Illumina II sequencing platform. After library construction, the library size was determined by 2% agarose gel electrophoresis, and the library concentration was measured by a Thermo Qubit 4.0 fluorescence quantification instrument.\u003c/p\u003e \u003cp\u003eThe raw image data files obtained by Illumina Hiseq\u0026trade; were converted into raw sequenced reads by CASAVA Base Calling analysis. The raw data quality values and other information were determined, and the quality of the sequencing data of the samples was evaluated visually using FastQC. The raw data were filtered using Trimmomatic, which included removing the following: sequences with N bases; splice sequences in reads; low-quality bases (Q-value\u0026thinsp;\u0026lt;\u0026thinsp;20) starting from the 3' to 5' direction of reads; low-quality bases (Q-value\u0026thinsp;\u0026lt;\u0026thinsp;20) starting from the 5' to 3' direction of reads; bases with quality values below 20 in the tails of the reads using the sliding window method (window size of 5 bp); and the reads themselves along with their paired reads with a length of less than 35 nt.\u003c/p\u003e \u003cp\u003eThe second-generation sequencing data were spliced using SPAdes, which first corrects the sequence errors of the original sequence, then assembles it by multiple Kmer values, and finally synthesizes the assembly results of each Kmer value to obtain the best results. Then GapFiller was used to complement GAP on the contig obtained from splicing, and finally PrInSeS-G was used for sequence correction to correct the editing errors and the insertion-deletions of small fragments during splicing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Gene element prediction\u003c/h2\u003e \u003cp\u003eGeneMark was used for gene prediction of the assembly results, with tRNAscan-SE for tRNA, RNAmmer for rRNA, and Rfam for snRNA, while RepeatModeler was used for the Denovo prediction of repetitive sequences of the assembly results. RepeatMasker was then used to find the position and frequency of occurrence of each type of repetitive sequences on the genomic segments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Gene function annotation\u003c/h2\u003e \u003cp\u003eThe protein sequences of the predicted genes were aligned with the NR, SwissProt, TrEMBL, COG, PFAM (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pfam.xfam.org/\u003c/span\u003e\u003cspan address=\"http://pfam.xfam.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and CDD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/cdd/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/cdd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases to obtain protein functional annotation using NCBI BLAST\u0026thinsp;+\u0026thinsp;V2.2.28 software. GO (Gene Ontology) functional annotations were obtained using the SwissProt and TrEMBL databases, and KEGG (Kyoto Encyclopedia of Genes and Genomes) annotations were obtained using KAAS V2.21 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Analysis of disease-causing and secondary metabolite-related genes\u003c/h2\u003e \u003cp\u003eThe gene set protein sequences were aligned to the CAZy database using HMMER3 to obtain their corresponding carbohydrate-active enzyme annotation information. The screening condition was E-value\u0026thinsp;\u0026lt;\u0026thinsp;1e-5. The gene protein sequences were aligned with the PHI-base database using BLAST to combine the annotation information of the genes and their corresponding pathogenic host interactions to obtain the final annotation results. Secondary metabolite synthesis gene clusters in strain PA-2 were predicted using the antiSMASH 3.0 online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fungismash.secondarymetabolites.org\u003c/span\u003e\u003cspan address=\"https://fungismash.secondarymetabolites.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SignalP software was used to predict the possible signal peptides of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and analysis","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Genome assembly\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 strain, and the genome of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 was assembled using the Illumina Hiseq\u0026trade; platform for the sequencing (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The total base length of all contigs of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 was 39,325,746 bp, with an average GC content of 48%, including 891 contigs with a total average length of 44,136.64 bp, and an N50 value of 155,640. The results of the genome sequencing of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 were uploaded to NCBI, with accession number PRJNA941260.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssembly results of \u003cem\u003eTrichoderma polysporum\u003c/em\u003e HZ-31\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEssential feature\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHZ-31\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39,325,746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN num\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax len\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e750,229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage len\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44,136.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContig num\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155,640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Gene element predictions\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Coding gene predictions\u003c/h2\u003e \u003cp\u003eGeneMark was used to predict the genes of the assembly results, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A total of 11,998 genes were predicted in the genome of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31, and the total length of the genes was 17,908,516, which accounted for 45.54% of the length of the whole genome of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31. The distribution map of gene lengths showed that the number of genes with lengths in the range of 800-1,000 bp was the largest, including 1,470 genes, and the number of genes with lengths of 0-200 bp was the smallest, including 84 genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The distribution plot of GC contents of the genes showed that the GC content ranged from 45\u0026ndash;55%(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), indicating that there was no significant bias in GC content.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of coding gene prediction results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll_num\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;=500bp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;=1000bp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN50\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax_len\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin_len\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAll_len\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean_len\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11,998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e69,120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17,908,516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1,492.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Repeat sequence prediction\u003c/h2\u003e \u003cp\u003eThe results of genome repeat sequence prediction of the sequenced strains are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The results showed that \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 contained 14,297 repeat sequences, with a total length of 1,149,690 bp, accounting for 2.94% of the total genome length. These sequences had an average length of 80.41 bp, of which 124 were DNA, 290 were LINE, 366 were LTR, 13 were SINE and 1,610 were unknown.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRepeat sequence statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRepeat family\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBase count\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage in genome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e341.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e398.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e329.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow_complexity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e82.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimple_repeat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e393372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e386182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll_RepeatSeq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1149690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.94%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Non-coding RNA predictions\u003c/h2\u003e \u003cp\u003eNon-coding RNAs are RNAs that do not code for proteins. Different strategies were used to predict the different non-coding RNAs with respect to their structural characteristics. An analysis of the results of the genomic data of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 shows that there are 148 transporter RNAs (tRNAs) and 45 ribosomal RNAs (rRNAs).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Gene function annotation\u003c/h2\u003e \u003cp\u003eThe predicted protein sequences of the genes were compared with the functional databases, and the annotation results of gene functional analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The numbers of annotated genes and the corresponding databases were: CDD 7,418, KOG 5,673, NR 11,541, PFAM 5,818, SwissProt 7,822, TrEMBL 11,531, GO 7,983, KEGG 3,841.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene function analysis annotated results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of unigenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in CDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in KOG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in NR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in PFAM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in SwissProt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in TrEMBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in GO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnotated in KEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal unigenes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 NR annotation results\u003c/h2\u003e \u003cp\u003eComparing the genomic genes of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 with the NR database(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), a total of 10,360 genes were annotated to the genus \u003cem\u003eTrichoderma\u003c/em\u003e. They accounted for 86.35% of the genome, indicating that strain HZ-31 indeed belongs to the genus \u003cem\u003eTrichoderma\u003c/em\u003e. Among them, the most genes were annotated to \u003cem\u003eTrichoderma gamsii\u003c/em\u003e with 3,229, followed by \u003cem\u003eTrichoderma atroviride\u003c/em\u003e with 2,771.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 KOG Functional classification annotation results\u003c/h2\u003e \u003cp\u003eThe genomic genes of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 were annotated to the KOG database(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The metabolic pathway with the highest number of annotated genes was General function prediction with 994; followed by Posttranslational modification, protein turnover, chaperones 460; Signal transduction mechanisms 338; and finally Secondary metabolites biosynthesis, transport and catabolism 333.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKOG functional classification statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKOG functional classification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene_num\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene_ratio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessing and modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromatin structure and dynamics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnergy production and conversion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell cycle control, cell division, chromosome partitioning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmino acid transport and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNucleotide transport and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarbohydrate transport and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoenzyme transport and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid transport and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranslation, ribosomal structure and biogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTranscription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplication, recombination and repair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell wall/membrane/envelope biogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCell motility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePosttranslational modification, protein turnover, chaperones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInorganic ion transport and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary metabolites biosynthesis, transport and catabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral function prediction only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunction unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignal transduction mechanisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracellular trafficking, secretion, and vesicular transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefense mechanisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtracellular structures\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnnamed protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNuclear structure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCytoskeleton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 GO functional classification annotation results\u003c/h2\u003e \u003cp\u003eThe predicted genes were categorized into cellular component, molecular function and biological process according to their functions in the GO database. The statistical results of gene functions and numbers of genes of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 annotated in the GO database are shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. There were 25,064 genes belonging to cellular components, 19 categories; 10,570 genes belonging to molecular function, 16 categories; and 24,740 genes belonging to biological process, 23 categories. Among them, the most annotated genes in cellular components are cell, with 5,713 genes; the most annotated genes in molecular functions are catalytic activity, with 4,429 genes; and the most annotated genes in biological processes are cellular processes, with 5,395 genes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGO functional classification statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOntology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene_num\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"22\" rowspan=\"23\"\u003e \u003cp\u003eBiological process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell killing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune system process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproductive process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological adhesion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulticellular organismal process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopmental process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocomotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle-organism process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRhythmic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive regulation of biological process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative regulation of biological process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of biological process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstablishment of localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-organism process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular component organization or biogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"18\" rowspan=\"19\"\u003e \u003cp\u003eCellular component\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtracellular region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleoid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtracellular matrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembrane-enclosed lumen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacromolecular complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganelle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtracellular matrix part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtracellular region part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrganelle part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirion part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembrane part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynapse part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell part\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSynapse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSymplast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e \u003cp\u003eMolecular function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein binding transcription factor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleic acid binding transcription factor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatalytic activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReceptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStructural molecule activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransporter activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectron carrier activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAntioxidant activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChannel regulator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetallochaperone activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnzyme regulator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein tag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranslation regulator activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNutrient reservoir activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecular transducer activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 KEGG functional classification annotation results\u003c/h2\u003e \u003cp\u003eThe genes of the \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 genome were annotated to the KEGG database into six major categories(Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), including Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases (HDP), Metabolism, and Organismal Systems, and they included 1083, 567, 1275, 1306, 4162, 1045 genes, respectively. Among these six categories, Metabolic processes had the most genes annotated, with Amino acid metabolism annotated to 836 genes; Carbohydrate metabolism annotated to 676 genes; Overview annotated to 570 genes; Lipid metabolism annotated to 459 genes; and Xenobiotics biodegradation and metabolism annotated to 432 genes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKEGG functional classification statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene_num\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eOrganismal Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExcretory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCirculatory system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndocrine system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironmental adaptation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigestive system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eMetabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolism of terpenoids and polyketides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnergy metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNucleotide metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCarbohydrate metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGlycan biosynthesis and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXenobiotics biodegradation and metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverview\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolism of cofactors and vitamins\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmino acid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e836\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolism of other amino acids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiosynthesis of other secondary metabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eHuman Diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndocrine and metabolic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInfectious diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrug resistance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCancers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeurodegenerative diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubstance dependence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eGenetic Information Processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFolding, sorting and degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e371\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReplication and repair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranslation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranscription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEnvironmental Information Processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMembrane transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignal transduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCellular Processes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell growth and death\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell communication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransport and catabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell motility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Analysis of secondary metabolite-related genes\u003c/h2\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Carbohydrate-active enzymes (CAZymes)\u003c/h2\u003e \u003cp\u003ePhytopathogenic fungi secrete a variety of carbohydrate-active enzymes, which are subdivided into different families according to their functions, such as Glycoside Hydrolases (GHs), Glycosyl Transferases (GTs), Polysaccharide Lyases (PLs), and Carbohydrate Esterases (CEs), Auxiliary Activities (AAs), and Carbohydrate-Binding Modules (CBMs) \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe protein encoding genes for \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 were annotated to the CAZy database with a total of 782 genes(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), the largest number of which were annotated to the Glycoside hydrolase family with 296 (37.85%); the smallest number of genes were annotated to the Polysaccharide cleavage enzyme family with 11 (1.41%). The remaining genes annotated to glycosyltransferases, sugar esterolytic enzymes, oxidoreductases, and carbohydrate-binding structural domains numbered 155, 139, 116, and 65 genes, respectively, for percentages of 19.82%, 17.77%, 14.83%, and 8.31%, respectively. The most genes annotated to the \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31GH family were the genes encoding GH18 with 35, followed by GH5, GH16, and GH3 with 21, 19, and 19, respectively. The most frequently annotated gene in the HZ-31GT family of \u003cem\u003eT. polysporum\u003c/em\u003e was the genes encoding GT41, with 50 genes, followed by GT32, GT2, and GT21, with 12, 11, and 8 genes, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Secondary metabolic gene clusters\u003c/h2\u003e \u003cp\u003eSecondary metabolites are key factors in the phytotoxic activity of pathogenic fungi, and a variety of phytotoxic secondary metabolites, including polyketides, non-ribosomal peptides, terpenes, and alkaloids, are used to kill host cells. A total of 67 gene clusters were identified in the genome of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The highest percentages were in the polyketide synthase gene clusters of type I (T1PKS), non-ribosomal peptide synthase-like gene clusters (NRPS-Like), peptide-like clusters synthesized and post-translationally modified in the fungal ribosome (fungal-RiPP-like), and non-ribosomal peptides (NRPS), and the highest percentage of terpenes (TERPENE), while NRP-metallophore accounted for less.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e BLAST comparison of all gene clusters of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 with known secondary metabolite gene clusters revealed that the 1421_g gene in the NRPS-Like, fungal-RiPP-like gene cluster type encoded material that was 100% similar to the choline gene cluster, while the 2916_g and 2919_g genes in the NRPS,T1PKS gene cluster type encoded material that was 66% similar to \u003cem\u003eC. albicans\u003c/em\u003e beauvericin. The similarity of the substances encoded by the 2916_g and 2919_g genes to beauvericin was 66%, while the 5536_g genes in the NRPS cluster type showed 100% similarity to verticillin. The 6364_g genes in the NRPS cluster type showed 100% similarity to the peramine/intermediate 1/intermediate 2 genes. The similarity of the 7085_g in the NRPS cluster type to enniatin was 100%, and the similarity of 7561_g in the NRPS cluster type to (-)-Mellein was 100%. The 7905_g gene encoded a substance which showed 100% similarity to trichoxide. The substance encoded by the 4736_g gene in the NRPS-like,T1PKS gene cluster type showed 50% similarity to swainsonine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 PHI pathogenicity related genes\u003c/h2\u003e \u003cp\u003ePHI annotation of the genomic genes of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 showed that a total of 757 genes were annotated in the database for pathogen-host interactions(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). When the pathogen genes were functionally categorized, the highest number of genes were annotated as reduced virulence, 370; followed by unaffected pathogenicity, 244; loss of pathogenicity, 82; lethal, 30; and resistance, 30; chemistry target: resistance to chemical, 15; and effector: plant avirulence determinant, 117, A total of 47 were annotated as increased virulence, hypervirulence. Among them, the GPA1 gene, with a relatively high number of annotations in the Pathogen-Host Interaction Database (PHIDB), was numbered gene9200 in the genome of strain HZ-31, with a total length of 1,062 bp. It encodes the G protein α subunit, which is related to the nutrient growth, sporulation, adherent cell formation, and toxin production of the fungus, and is involved in the pathogenicity of \u003cem\u003eCryptoccus neo formans, Aspergillus nidulans\u003c/em\u003e, \u003cem\u003eUstilago esculenta\u003c/em\u003e, \u003cem\u003eFusarium graminearum\u003c/em\u003e and \u003cem\u003eT.harzianum.\u003c/em\u003e The knockdown of the GPA1 gene could cause the complete loss of pathogenicity of \u003cem\u003eF. graminearum\u003c/em\u003e GPA1 mutant on wheat spikes \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Further studies have also confirmed that the GPA1 gene can affect adherent cell formation and the expression of several virulence-related genes associated with infestation through the regulation of intracellular cAMP levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.4.4 Signaling peptide proteins\u003c/h2\u003e \u003cp\u003eA total of 1,023 signal peptide proteins (8.53%) were predicted for \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31(Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSignal peptide protein prediction statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrganism type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal protein number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSignal proteins from SignalP-TM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignal proteins from SignalP-noTM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal sigan proteins\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignal protein ratio (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeuk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.526421\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e \u003cem\u003eTrichoderma polysporum\u003c/em\u003e HZ-31 is a microbial fungus with great potential for weed control, and it is difficult to comprehensively analyze the mechanism of action of \u003cem\u003eT. polysporum\u003c/em\u003e by traditional experimental and identification methods. In this regard, an in-depth study of the intrinsic causes of \u003cem\u003eT. polysporum\u003c/em\u003e pathogenicity at the genomic level is of great significance. Therefore, we obtained the genome size of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 by whole genome sequencing and bioinformatics analysis as 39,325,746 bp, with 48% GC content, and the number of coding genes was 11,998. Among these genes, 148 tRNAs and 45 rRNAs were predicted in the annotated GO, COG and KEGG databases as related to amino acid metabolism, carbohydrate metabolism and lipid metabolism.\u003c/p\u003e \u003cp\u003eA variety of carbohydrate-active enzymes secreted by plant pathogens are involved in the degradation of host plant cell walls. Several studies have shown that pathogens from animals and plants utilize carbohydrases and other nutrients to regulate their virulence and adjust their metabolism for successful colonization \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. A total of 782 genes were annotated in the carbohydrase database in this study. Members of the glycoside hydrolase family act as virulence factors and modulate plant immune responses during pathogen infection \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Among them, the gene encoding GH3 was annotated in \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31, and it was found to encode a β-glucosidase that plays an important role and is a key enzyme in cellulose degradation, which is closely related to the pathogen's infectious characteristics. Wang et al.\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e found that the cell wall degrading enzyme of \u003cem\u003eZiziphus jujuba melanogaster\u003c/em\u003e, which plays a key role in the pathogenic process, is β-glucosidase, and its activity is the highest in the diseased-healthy junction in the process of infection. Li\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e found that there were 16 GH3 gene family members in the genome of \u003cem\u003eAspergillus sphaericus\u003c/em\u003e, and the transcripts of most of them were up-regulated under the induction of cellulose, which was consistent with the changes in extracellular β-glucosidase activity. Studies have suggested that the GH3 gene family in \u003cem\u003eXylaria\u003c/em\u003e plays an important role in cellulose degradation and plant pathogenicity.\u003c/p\u003e \u003cp\u003eGlycosylation is an important post-translational modification of proteins, which can affect the solubility, stability and catalytic activity of proteins, and also has important biological functions related to protein folding, localization and translocation. In recent years, a growing number of studies have demonstrated that glycosyltransferases are closely related to pathogenic virulence and play key roles in biological processes such as the adhesion, immune escape and colonization of pathogenic bacteria. The gene encoding GT2 of the glycosyltransferase family in \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 was annotated, and GT2 was shown to be involved not only in biomass synthesis, but also in many complex aspects of cellular processes in fungi. Zhang et al.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e used CRISPR/Cas9 and homologous recombination techniques involving deletion and backfilling of the PaGt2 gene of the GT2 family encoding glycosyltransferase, and found that the strain was significantly inhibited in nutrient growth, did not produce conidiophores and conidia, and had significantly reduced pathogenicity on peach shoots and fruits.\u003c/p\u003e \u003cp\u003eGenomics, molecular biological and bioinformatics studies have shown that the genes encoding enzymes which produce various fungal secondary metabolites are clustered and often in close proximity to telomeres \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The genes that are found in clusters of secondary metabolite synthesis genes are frequently co-regulated according to the functions of the secondary metabolites encoded by these genes \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Furthermore, an increasing number of secondary metabolite synthesis genes are thought to be closely related to, or even regulate, the pathogenicity of pathogenic bacteria. In this study, the \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 secondary metabolite synthesis gene cluster was annotated to genes that synthesize toxins such as enniatin, beauvericin and Mellein. Beauvericin is a non-specific phytotoxin with toxic effects on many cell lines, and the essential components in its synthesis are the amino acids L-Phe, D-HYIV, ATP/Mg\u0026thinsp;+\u0026thinsp;and ADOMet \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The mechanism of its cytotoxicity involves its role as a K\u003csup\u003e+\u003c/sup\u003e ion carrier, in which it can be embedded in biological membranes, forming channels, triggering the elevation of Ca\u003csup\u003e2+\u003c/sup\u003e in the cytoplasm, affecting the electrochemical gradient of the cell membrane, and ultimately inducing a series of cytotoxic reactions. Beauvericin can also enter the nucleus of plant cells, combine with DNA to form DNA-BEA complexes, and through calcium-dependent endonuclease cleavage of the bound DNA, it can interrupt chromosomes and cause toxicity. Chen\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e found that knockdown of the leukocidin homologous gene FOXB_16250 in \u003cem\u003eFusarium spinosum\u003c/em\u003e Fo5176 resulted in a reduction in the pathogenicity of the Fo5176 mutant, as well as a delay in the onset of disease in the mutant inoculated into Columbia-type \u003cem\u003eArabidopsis thaliana\u003c/em\u003e wild-type plants, suggesting that leukocidin synthesis genes inhibit \u003cem\u003eFusarium spinosum\u003c/em\u003e pathogenicity. Enniatin is a hexapeptide fungal toxin that is present in the mycelium and can have a strong toxic effect on the cellular tissues of plants [28]. The esyn1 gene is an important regulator of the biosynthesis of enniatin. Chen et al.\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e cloned the esyn1 gene from Foc4, and compared with the wild strain, the biosynthesis of \u003cem\u003eFusarium enantiospirillum\u003c/em\u003e was significantly reduced in the knockout mutant. Furthermore, the pathogenicity of the mutant was completely lost, whereas backfilling of the wild strain was able to restore the biosynthesis and pathogenicity of \u003cem\u003eFusarium enantiospirillum\u003c/em\u003e, which suggests that this gene is a key factor in the pathogenic bacterial infections of the plant. Mellein is a known compound with various phytotoxic, cytotoxic, fungicidal, antimicrobial and larvicidal activities reported \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Li et al.\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e demonstrated the presence of (R)-(-)-mellein in the fermentation broth of \u003cem\u003eVitis vinifera\u003c/em\u003e and found that it was also present in \u003cem\u003eVitis vinifera\u003c/em\u003e-infected apple fruits and twigs, and that there was a relationship between lesion expansion and honey curdling mycorrhizal fungal pigmentation in the apple tissues. Phytotoxicity bioassays have shown that honeystrobin causes discoloration and death of apple leaves and browning of stems. Another study showed that the main components of the toxin of \u003cem\u003eSphaeropsis sapinea\u003c/em\u003e, a pine tree dieback disease, are also the above two forms of R-(-)-Mellein and 4-hydroxyMellein, in which R-(-)-Mellein plays a major role, while the other two are synergistic with each other in the toxin and antifungal activity assays \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. These secondary metabolites play a key role in the pathogenicity of pathogens in their host plants, and the gene clusters that regulate the synthesis of secondary metabolites are fundamental in the regulation of that pathogenicity. The gene clusters of secondary metabolite synthesis annotated in the present study may also play important roles in the pathogenicity of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 in weeds.\u003c/p\u003e \u003cp\u003eBy sequencing the whole genome of the \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 strain, all the genetic information for the genome of this pathogen was obtained. Many virulence-related pathogenic genes were found, which were mainly involved in cell wall catabolic enzymes, strain nutrient growth and biomass synthesis, etc. The genome information also showed that \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 contains a large number of genes involved in toxin biosynthesis, suggesting that \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 is able to produce a variety of toxins during the infestation process. The present study bridges the gap in the genomic information of this strain, and also provides the necessary genetic background information for further analyzing the herbicidal mechanism of this strain.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e \u003ch2\u003eContributions\u003c/h2\u003e \u003cp\u003eYushan He and Haixia Zhu conceived the study, collected samples, and did lab steps. Yushan He analysed the results and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH. and Z. conceived the study, collected samples, and did lab steps. H. analysed the results and wrote the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe research was funded by basic research project (2024-ZJ-928) of Science and Technology Department of Qinghai Provincethe. We are also grateful to the anonymous reviewers for their valuable suggestions and comments.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe sequence data supporting the results of this study has been stored in the NCBI with the main entry code PRJNA941260\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMenalled, U.D., Smith, R.G., Cordeau, S. et al. Phylogenetic relatedness can influence cover crop-based weed suppression. Sci Rep 13, 17323 (2023). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-023-43987-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-023-43987-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong ,W.M. Weed community composition and its prevention and control countermeasures in Qinghai oat field[D]. 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Chinese Journal of Forest Diseases and Insects,2010,29(02):31\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"whole genome, T. polysporum HZ-31, weeds, key genes","lastPublishedDoi":"10.21203/rs.3.rs-4124222/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4124222/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to resolve the key genes for weed control by \u003cem\u003eTrichoderma polysporum\u003c/em\u003e at the genomic level, we extracted the genomic DNA and sequenced the whole genome of \u003cem\u003eT. polysporum\u003c/em\u003e strain HZ-31 on the Illumina Hiseq\u0026trade; platform. The raw data were cleaned up using Trimmomatic and checked for quality using FastQC. The sequencing data were assembled using SPAdes, and GeneMark was used to perform gene prediction on the assembly results. The results showed that the genome size of \u003cem\u003eT. polysporum\u003c/em\u003e HZ-31 was 39,325,746 bp, with 48% GC content, and the number of genes encoded was 11,998. A total of 148 tRNAs and 45 rRNAs were predicted. A total of 782 genes were annotated in the Carbohydrase Database, 757 genes were annotated to the Pathogen-Host Interaction Database, and 67 gene clusters were identified. In addition, 1023 genes were predicted to be signal peptide proteins. The annotation and functional analysis of the whole genome sequence of \u003cem\u003eT. polymorpha\u003c/em\u003e HZ-31 provide a basis for the in-depth study of the molecular mechanism of its herbicidal action and more effective utilization for weed control.\u003c/p\u003e","manuscriptTitle":"Whole genome sequencing and analysis of the weed pathogen Trichoderma polysporum HZ-31","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-09 10:56:13","doi":"10.21203/rs.3.rs-4124222/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-29T08:26:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-25T07:48:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-19T16:07:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a39640d5-7062-47f1-acbb-e8f6f5d6af6b","date":"2024-04-11T08:11:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84cac099-c70d-4f66-8010-ba9968f725ef","date":"2024-04-10T13:38:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-10T06:44:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-10T06:42:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-09T11:20:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-04T07:47:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-18T14:57:56+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":"9b88f490-7a6a-4e77-a708-9578c3ab4303","owner":[],"postedDate":"April 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":30252583,"name":"Biological sciences/Microbiology"},{"id":30252584,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2024-07-03T00:31:36+00:00","versionOfRecord":{"articleIdentity":"rs-4124222","link":"https://doi.org/10.1038/s41598-024-66041-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-07-02 00:31:36","publishedOnDateReadable":"July 2nd, 2024"},"versionCreatedAt":"2024-04-09 10:56:13","video":"","vorDoi":"10.1038/s41598-024-66041-w","vorDoiUrl":"https://doi.org/10.1038/s41598-024-66041-w","workflowStages":[]},"version":"v1","identity":"rs-4124222","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4124222","identity":"rs-4124222","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}