Actinomycetes Strain Selection From Maize Rhizosphere With Antagonistic Potential Against Fungal Phytopathogen

Actinomycetes Strain Selection From Maize Rhizosphere With Antagonistic Potential Against Fungal Phytopathogen | 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 Research Article Actinomycetes Strain Selection From Maize Rhizosphere With Antagonistic Potential Against Fungal Phytopathogen Oghoye Priscilla Oyedoh, Ayansina Segun Ayangbenro, Olubukola Oluranti Babalola This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5385478/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 May, 2025 Read the published version in Biologia → Version 1 posted 5 You are reading this latest preprint version Abstract Fungal infestation in maize reduces productivity by 80%, with leaf blight disease causing about 60% reduction in grain yield. Numerous studies have shown the efficacy of synthetic chemicals in reducing the disease severity in agro-systems, which was efficient but with several negative impacts. Hence, there is an urgency to search for a more sustainable alternative with similar or better efficiency. This study was conceptualized to select a strain with in vitro antagonistic activity against leaf blight causative fungi and predict the secondary metabolites produced through the culture-dependent method and whole genome sequencing approach. Maize pathogens, Bipolaris sp., Fusarium equiseti , and Phoma sp., were obtained from symptomatic leaves and known to cause leaf blight diseases in maize crops, and antagonized by Streptomyces sp. OP7. The OP7 strain was isolated from the rhizosphere of maize crop and its cell-free supernatant extract showed antifungal activity against phytopathogens tested. The complete whole genome data of Streptomyces sp. OP7 revealed the presence of 16 biosynthetic gene clusters similar to metabolites with antifungal functional annotations implicating Strep tomyces sp. OP7’s capacity to produce valuable agroactive compounds. Maize leaf blight Streptomyces sp. secondary metabolites antifungal activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Maize is the largest local field crop produced in South Africa, with a gross income value of about 1.94 billion US dollars as of 2018/2019 (Cowling, 2023). The delicate challenge of crop productivity in South Africa (due to climate shocks resulting in extreme weather conditions and high evapotranspiration) by 2080 has been projected to reduce cereal productivity by 15–50%. In addition, this climate change will cause an evolutionary shift in the structure and function of microbial communities, causing most pathogens to be dominant, which could lead to diseases (UNEP et al., 2009, Nhemachena et al., 2020). Phaeosphaeria leaf spot, common rust, northern leaf blight, and gray leaf spot have been reported as causative agents of high leaf blight disease pressure in smallholder farming environments in South Africa, which poses a threat to maize food security. Aside from the mentioned, 'Helminthosporiod groups' such as Bipolaris sp., Curvularia spp., Drechslera sp., and Exserohilum sp. and other conidia producing Ascomycetes like Fusarium and Phoma spp. are known to cause leaf blight diseases in maize and cereal crops (Nsibo et al., 2019, Berger et al., 2020, Craven et al., 2020). Some of these pathogens have been reported to cause maize blight with diseases commonly controlled in crops using resistant cultivars or synthetic chemicals. While through rural development programs, these resistant cultivars could be freely disbursed to farmers, an extra input production strategy that involves precise and timely management such as weeding, fertilizer application, and pesticide application is accompanied by its application, which is not cost-effective. In addition, the alternative application of synthetic chemicals could be a non-sustainable, polluting manner of enhancing resistant phytopathogenic fungal communities in the field, which affects public health and the surroundings (Alipour Kafi et al., 2021). To assist farmholders in achieving a profit margin, a sustainable mode of phytopathogen elimination has to be adopted during maize production. Previous meta-studies of bacterial structure and function revealed the dominance of Actinomycetes in disease-suppressive rhizosphere soil and leaf blight-infected maize, owed to their ability to portray antagonistic functionality through metabolite production. A 10X meta-study accompanied by metabolites prediction conducted by Tracanna et al. (2021) using antiSMASH (the antibiotics and secondary metabolites analysis shell) tools revealed some antifungal metabolites in a root soil suppressed by Fusarium culmorum , with some metabolites been associated with Streptomycetes bacteria. In addition, the structural and functional profiling of microbial diversity in plant environments revealed Actinomycetes and metabolites prevalence, including enzymes, volatile organic compounds, hydrogen cyanide, organic acids, antibiotics, and siderophores (Babalola et al., 2022). This suggests the luxuriant availability of this phylum in the rhizosphere of plants and their possible role in disease management. Hence, a possible strategy is selecting a native Actinomycetes strain adapted to maize environmental conditions (the rhizosphere). The rhizosphere of plants is the soil area beneath the root, which experiences constant root exudates, soil, and microbial interactions for plant health. The beneficial microbes involved in plant health assurance include bacteria, fungi, and archaea in a particular order of abundance. Bacteria assure plant health against diseases through metabolite secretion, particularly antibiotics, to directly antagonize pathogen proliferation (Adegboye and Babalola, 2013, Adedeji and Babalola, 2020). In addition, numerous studies have revealed the inherent hub of gene clusters in Actinomycetes, which are conserved coding regions for known and unknown metabolites of various biotechnological applications, especially in the agricultural sector (Chávez-Avila et al., 2023, Oyedoh et al., 2023, Thirugnanam et al., 2023). These metabolites are categorized based on their biosynthetic gene clusters, which are polyketides, non-ribosomal peptides, lasso peptides, saccharides, aminoglycosides, terpenoids, and ribosomally synthesized and post-translationally modified peptides RIPPs (ribosomally synthesized and post-translationally modified peptides) (Veilumuthu et al., 2022). These biosynthetic genes are clustered in the genome and have modular domains or enzymes that function in an assembly line to produce complex biomolecules. Therefore, one could predict the underlying mechanism of disease inhibition through the metabolites harboured with antifungal properties (Tracanna et al., 2021). This study is designed to identify the biosynthetic gene clusters through genome mining after selecting a potential antagonistic strain from the rhizosphere of maize plants. Materials and Methods Sampling Collection, Preparation and Isolation Maize plant rhizosphere-soil samples were collected from the School of Agriculture Research Farm, North-West University in Molelwane, Mahikeng, North West Province, South Africa. An arid environment affected by drought, with a coordinate of 25 O 47 I 26.5 II N25 O 37 I 04.1 II E and stored at -20 o C. In addition, symptomatic leaves were randomly collected from a maize-producing area at the farm at the vegetative stage of growth. The samples were placed in sterile containers, and transported to the laboratory for processing. Diverse seed cultivars (Wema 3128, BG685R5, Pan 6479, Pan 5R-ZBH6, and Pan 5R-2CP8) were also collected from the university farm for planting. To reduce the growth of fast-growing bacteria in the samples before culturing, the rhizosphere soil samples were physically, chemically, and synergistically pretreated. Pretreatment was conducted physically by heating at 100 O C for 1hr, chemically diluting in 9ml of 1.5% phenol solution, and synergistically combining both treatments (Ayoib et al., 2023). In contrast, the non-pretreated samples served as the control. The pretreated aliquot was plated on modified Actinomycetes isolation agar (AIA) prepared with five mM phosphate buffer at neutral, acidic, and alkaline pH and supplemented with cycloheximide/nalidixic acid to isolate wider Actinomycetes diversity. Pure and discrete colonies of Actinomycetes isolates were putatively identified using morphological (Ayoib et al., 2023) and selective biochemical tests such as motility, catalase, hydrogen utilization, indole, and oxidase tests. Actinomycetes-like isolates were stored at -80 O C temperature with glycerol (20%). Pathogen Isolation, Identification, and Invitro Pathogenicity Test Prior to the pathogenicity test, maize seeds (Wema 3128, BG685R5, Pan 6479, Pan 5R-ZBH6, and Pan 5R-2CP8 cultivars) were collected from the University farm. They were confirmed viable by conducting viability and pre-germination tests. The seeds were soaked in 500ml sterile distilled water, placed in a beaker, and troubled with hands. Then, the seeds above the water were discarded, while those that sank below the water were used for a pre-germination test. Before the pre-germination test, the seeds were surface sterilized in 1% hypochlorite and then rinsed with sterile distilled water thrice until the chemical was removed. The seeds were air-dried under sterile conditions in a laminar flow cabinet (Filta-Matix Laminar Flow Cabinets). Then 50 seeds were plated on a paper towel infused with 10ml water in sterile Petri dishes (10 seeds per plate). The Petri dishes were covered with another 10ml infused paper towel and incubated at 30 O C for seven days. After this, the percentage of germination was determined using the formula described below: Percentage Seed Germination (%) = \(\:\raisebox{1ex}{$\text{n}$}\!\left/\:\!\raisebox{-1ex}{$\text{N}$}\right.\times\:100\) Where n = number of germinated seeds after seven days, N = total of seeds In sterile bags, diseased maize leaves showing symptoms of early blight at the vegetative stage of growth were collected from the university farm in Molelwane and processed within a few hours after sampling. Symptomatic maize leave (WEMA 3128 cultivar) cut into 2cm pieces were sterilized with 1% sodium hypochlorite for 1mins, washed thrice in sterile distilled water, then dried on sterilized filter paper and plated on a potato dextrose agar plate containing 0.6ml of 0.5mg/l chloramphenicol. The fungal colonies growing around each bit were assessed (this isolation was conducted in triplicate to confirm culture obtained) and sub-cultured. Three isolates were repeatedly obtained from all symptomatic leaves and stained with lactophenol cotton blue, and viewed under a photographed microscope with 40X magnification based on cultural (shape, size, colour & texture) and morphological characteristics (shape, size, and colour of conidia). The soil was sieved with a 2mm diameter filter and weighed before sterilization at 121 O C for 15mins. The soil was moistened to a maximum retention capacity (MRC- the maximum water quantity the soil can absorb without being flooded) of 2/9th substrate water for 18hrs before sowing. Then five seeds were sown per pot in 5cm depth and 2cm apart, the seed holes were closed, and pots were kept in the greenhouse. The pots were maintained at 48hrs intervals by watering, and after ten days, the least vigor plant per pot was removed. Under greenhouse conditions, the average day and night temperatures were 25 O C and 18 O C. Then foliar treatment was carried out by spraying the abaxial surface of leaves with a spore suspension of pathogens 10 5 spores/ml. At the same time, sterile water was used as control. The spraying was done at the 5–6 leaf stage, 20ml of conidia suspension was evenly sprayed and the pots were kept humid for 18hrs (by covering them with polythene bags). After 18hrs, the plants were removed from the humid environment and kept in a greenhouse. The inoculated and non-inoculated foliage were examined, and then compared; from the leaves which exhibited symptoms, the organism was re-isolated and the culture obtained was compared with the original isolates to confirm the identity based on Koch's postulate. Disease severity was assessed at 7-day intervals by using a 1–5 disease rating scale, where 1 = no symptoms; 2 = moderate lesion development below the leaves; 3 = heavy lesion development on and below the leaf with a few lesions above it; 4 = severe lesion development on all but the uppermost leaves, which may have a few lesions; and 5 = all leaves dead (Aregbesola et al., 2020). After which the mean of the severity ratings was used to assess the virulence of each isolate, and the experiment was conducted twice, in duplicate. Also, the disease incidence was determined, which is \(\:\frac{\text{n}}{\text{N}}\times\:100\) Where n = number of diseased plants, N = total number of plants Primary and Secondary Screening All putative isolates were subjected to primary screening described by Jing et al. (2020), with some modifications. Briefly, yeast-malt agar (prepared with yeast, malt, glucose, and agar-agar) plates were streaked with Actinomycetes isolates longitudinally and incubated for two days. Then, the selected maize leaf blight causative fungal isolates were diagonally streaked across the Actinomycetes isolates to determine the inhibitory effects of Actinomycetes whole cell against fungal isolates, and isolates with activity were selected for further screening. The zone of inhibition was measured in cm for the isolates that displayed a zone of clearance. However, some isolates that did not display a zone of clearance but inhibited mycelium formation were designated A- for active, AA for more active, and AAA for most active. This screening was conducted to determine the inhibitory effect of supernatant cell-free extract obtained from active isolates from previous screening against the test pathogens. Briefly, the supernatant extract from each Actinomycetes isolate, after centrifugation at 12,000rpm for 10mins at 4 O C, was then filtrated by syringe Millipore filter 0.22µm. Antifungal activity of the cell-free filtrated supernatant against the isolated plant pathogen was evaluated using the food poisoning techniques and mycelial growth rate method described by Wang et al. (2020). Briefly, the supernatant extract (25ml) and potato dextrose agar (75ml) were mixed (after cooling the agar to 45 O C) in sterile Petri dishes and allowed to solidify. Then 0.6cm of each fungal isolate was transferred into the center of solidified PDA plates supplemented with supernatant in duplicates. PDA plates inoculated without supernatant extracts served as control and all plates were incubated at 25 O C until control plates were completely grown. After incubation, fungal mycelial growth was observed and expressed as percentage growth inhibition as described below. Inhibitive % = (control diameter –treated diameter/control diameter) X 100 Molecular Identification of Selected Active Bacterial and Fungal Isolates Based on their antifungal activity against the phytopathogens, 14 out of 23 Actinomycetes isolates were chosen for molecular analysis. Then, three phytopathogens manifesting diverse levels of disease severity were also identified. Actinomycetes and fungal DNA were extracted using the Quick-DNA™ Miniprep Kit, particularly for bacteria and fungi, manufactured by Zymo Research, Irvine, CA, USA, following the manufacturer's protocol. The purity of the DNA was determined using the Thermo Fischer Scientific, CA, USA-manufactured NanoDrop spectrophotometer. The pure DNA was amplified using 25µl reaction mixture containing 2µl of template DNA, 2µl of primer (specific primers of Actinomycetes 27F (5'-AGAGTTTGATCCTGGCTCAG-3') (Bruce et al., 1992) and 16Sact1114R (5'-GAGTTGACCCCGGCRGT-3') (Martina et al., 2008)), 8.5µl nuclease-free water and 12.5µl OneTaq Quick-Load 2* master mix with standard buffer (New England Biolabs NEB) in a PCR (polymerase chain reaction) of pre-denaturation at 95 O C for 2mins, denaturation at 95 O C for 30secs, annealing at 53 O C for 30secs, extension at 72 O C for 1min and final extension at 72 O C for 7mins in 30cycles. The selected phytopathogens were identified using 12.5µl master mix, 2µl DNA, 9.5µl nuclease-free water and 1µl primer (ITS1 (5′-TCCGTAGGTGAACCTGCGG-3′) and ITS4 (5′ TCCTCCGCTTATTGATATGC-3′)) in a PCR analysis involving denaturation at 95 O C for 5mins, extension at 95 O C for 30secs, annealing at 50 O C for 45secs, elongation at 75 O C for 1min and final elongation at 72 O C at 10mins (Daccò et al., 2020). The PCR amplicon's purity was certified through electrophoresis using 1.5% (w/v) agarose gel. The PCR products were purified following the manufacturer's protocol on the ExoSAP-IT (Applied Biosystems, Foster City, CA, United States) kits. The pure amplicons were sequenced at Inquaba Biotec Ltd, Pretoria, South Africa. The sequenced 16S rRNA genes were compared with the sequences of identified organisms on the NCBI database. The phylogenetic trees were constructed using a neighbor-joining method of MEGA 6.0 (Kumar et al., 2018), and the distance of evolution was calculated using the maximum-parsimony algorithm and using the bootstrap analysis on 1000 replicates level of confidence. Following the standard Illumina sequencing method at Novogene, Singapore, the whole genome sequencing of the rhizospheric Actinomycetes isolate M12 was performed on Illumina Novoseq 6000 platform. After FastQC quality sequence assessment, low-quality sequences and adaptors were trimmed with the trimmomatic software (Bolger et al., 2014). The genomic sequences of high-quality reads were analyzed on the KBase platform (Arkin et al., 2018). The processed sequences were further assembled with the Spade (Nurk et al., 2013), the assembled genome was annotated using RASTtk software servers (Allen et al., 2017), and functional annotation was done using BlastKOALA software housed by KEGG, all with default settings. Making use of recently updated features, taxonomic analysis was conducted for the whole genome data using GTDB and typed strain genome server (TYGS) by comparing the user's genome against GTDB database and ten typed strains genome, respectively (Meier-Kolthoff and Göker, 2019, Parks et al., 2022). The pan-genome of the user’s strain was analyzed against the most similar reference genome with full genome data on the NCBI RefSeq bank. Then, to predict the biosynthetic gene clusters of the annotated genes, antiSMASH 3.0 was used, and PRISM 4.0 detected the structural scaffold (Blin et al., 2023, Skinnider et al., 2020). Finally, the gene clusters detected with antiSMASH 3.0 were blasted with NCBI and UniProt software to confirm the cluster. Results In this study, 58 Actinomycetes-like isolates were isolated from healthy maize rhizosphere obtained from the sampling location. Most of these isolates were derived from non-pretreated samples at pH 7 and 10, with two isolates coded M2 and M1 obtained from chemically pretreated samples at pH 10. The isolates were subjected to microscopic and selected biochemical characterization, and about 23 isolates were confirmed to be Actinomycetes (Table S1 and Figure S1). However, for the test isolates obtained from diseased leaves based on their macroscopic and microscopic analyses, the first fungal isolate was fluffy cream with a reverse brownish colour and slender, longitudinal conidia (Figure 1c); the second isolate is black front and reverse with short, oval, single, conidia morphology as shown in Figure 1b, while the last isolate on Figure 1a has glittering-like gray front with black reverse colour and smaller, single ovoid morphology but with denser conidiophores. Then, to confirm the pathogenicity of the test isolates, the most viable maize seeds among the cultivars collected from the University Farm, which is Wema 3128, with a 100% germination rate (Table 1 ), were planted in a greenhouse. Table 1 Pre-germination test Seed Cultivars Prepared Seeds Covered Seeds Germinated Seeds Percentage Germination (%) Wema 3128 50 50 50 100 BG685R5 50 50 30 60 Pan 6479 50 50 0 0 Pan 5R-ZBH6 50 50 10 20 Pan 5R-2CP8 50 50 5 10 The Fusarium -treated leaves (S-W), displayed coalescence of lesions after 7 days of treatment and had the highest disease severity. For Bipolaris (N-B) treated leaves, a pin-hole-like lesion appeared within three days at the bottom leaflets and spread upward, then expanded into an elongated strip that transformed into chlorotic leaves after 14 days. The Bipolaris treated leaves had a high disease percentage incidence of about 80% after seven days of treatment, as indicated in Table 2 . For Phoma -treated (C-Z-M) leaves a brown-like ellipsoid lesion appeared on the leaves (Fig. 2 ) with a high disease incidence of 73.33%, which becomes longer after seven days and then transforms into chlorotic leaves after the 14th day. After 21 days of treatment, the lower leaflets were already showing chlorotic symptoms, which began from the elongated strip lesion area and the edges of the leaf. Leaves sprayed with Fusarium equiseti and Bipolaris sp. showed leaf blight-like lesions than leaves sprayed with Phoma sp., while the control revealed no sign of disease. However, further experiments were conducted with the two leaf blight causative strains and Phoma sp., based on the fact that the last has been reported as a maize pathogen. Table 2 Invivo pathogenicity confirmatory test Isolate Percentage disease incidence Total Disease Severity Code 7days 14days 21days 28days 7days 14days 21days 28days Total Control 20.00 20.00 33.33 26.67 25.00 a 1 1 2 2 1.5 a C-Z-M 20.00 20.00 73.33 53.33 41.67 b 1 1 3 2 1.75 b N-B 20.00 26.67 80.00 53.33 45.00 b 1 1 3.33 3 1.98 d S-W 33.33 40.00 86.67 66.67 56.67 c 1 1 4 4 2.08 c values followed by different lowercase letters within a column are significantly different according to the least significant difference test (P < 0.05) All putative Actinomycetes isolates were subjected to an initial primary screening (Table S2) against the two leaf-blight causative maize pathogens and Phoma sp. (on the basis that Phoma sp. is a reported pathogen of other crops of economic benefits). Here, Actinomycetes whole cells with high antagonistic activity include M1, M6, M4, M2, M7, and M12. Figures 3 a & b show the antagonistic activity of M12 and M6 streaked longitudinally against the three pathogens streaked diagonally, reflecting whole-cell growth of the Actinomycetes isolates into the pathogens, hence antagonizing the pathogen's growth, except for Fig. 3 c that displays Actinomycetes inactivity against the pathogens. For secondary screening (Figure S2) with 50% extract, there was no significant difference between Actinomycetes isolate coded M12 and M14, but with 25%, only M12 remains constantly active against the pathogens, inhibiting pathogenic growth at a rate greater than 80% (for 50% extract); hence it was selected for whole genome sequencing, while M6 was more active against Phoma sp. at both concentration. Aside from isolate M12 identified as Actinomycetales bacterium , which displayed the highest inhibitory level against the pathogens at both concentrations with accession number PP564474, isolate M2, M4, M5, M6, M7, and M14, M13, M16, and M17, inhibited the growth of the pathogens at diverse degree shown in Table S5 bearing accession numbers PP564468, PP564469, PP564470, PP564471, PP564472, PP564473, PP564475, PP564476 and PP56447 respectively. In addition, strain M1 could not be given an accession number due to its low percentage homology of 81% to its closest reference bacteria, Strep tomyces erythrogriseus . In addition, the test pathogens C-Z-M, N-B, and S-W were identified as Phoma sp. Bipolaris sp. and Fusarium equiseti (Fig. 5 ), with accession numbers PP563710, PP563711 and PP563712, respectively. The assembled genome of the most active Actinomycetes isolate-M12 was annotated with Rast pipelines and had a genome size of 7109246, 37 contigs, 59222 ORFs composed of scaffolds with an N50 of 563188bp and a DNA GC content of 72.4%. The genome encoded 6624 coding regions, 66 tRNA, 64 mRNA regions, and 6452 (97.4%) predicted genes with 192 functional roles. In addition, the subsystem categories in the annotated genome and their corresponding feature numbers are highlighted in Table S4. Based on the Rasttk annotation, the subsystem functions revealed the presence of 24-iron acquisition and metabolism proteins, 337-amino acid and derivative, 61-stress response, 110-protein metabolism, 17-nitrogen metabolism, 7-potassium metabolism, 7-sulphur metabolism, 295-carbohydrate metabolism, 126-isoprenoid and fatty acid 24-regulation and cell signaling, 13-dormancy and sporulation functionalities. Additionally, using the KEGG-housed BlastKaola software, the functional annotation, pathway, and molecular function identifier also annotated 2431 coding regions (38.7%), with 4193 (63.3%) been an uncharacterized or hypothetical proteins. The genes predicted include fep (D, G, C); fag (A, B, C), cch (C, D, E), act I, akn (B, C, D) ent D, dhb F,and des (H, G, F) genes coding for siderophore biosynthesis; spr (B, D) for streptogrisin biosynthesis; cpb (D, E) for chitinase biosynthesis; acpS involved in mycolic acid biosynthesis; fab F for fatty acid biosynthesis; ect (A, B, C, D) for ectoine biosynthesis; phz E for phenazine biosynthesis; kor B, hem (C, B), his C, cob A, hem D, thr (B, C), mqn (E, C), cyo E, cta B, dap A, fab G, acp (P, M) for secondary metabolites biosynthesis. Other genes characterized aid in nitrogen fixations ( nif U, nas A, B, nir B, D, npd , ncd and nar K); Fe-sulphur biosynthesis ( suf B,C,D,S, dap A, D); proline biosynthesis ( pub B, pro DH); heat shock proteins ( htp X); plant-pathogen interaction ( Tuf , htp G); thermogenesis ( cta B, cyo E) and quorum sensing ( sec E, ddp FD). The genome study of the most active isolate M12 revealed that the strain is related to its closest homolog, Streptomyces sp. DH-12, with an ANI of 95.96 and Streptomyces tendae ; therefore, the isolate was identified a s Streptomyces sp., as represented in Fig. 7 . The phylogenetic relationship based on the full-length genome sequencing conducted with type strains genome server (TYGS) revealed closest similarities with Streptomyces matensis JCM 4277 with a digital DNA-DNA hybridization value of 40.4%, which is less than 70% and delineates possibly new species (based on TYGS only). The annotated genome of Streptomyces sp. OP7 contains 16 biosynthetic gene clusters (Fig. 8 A) with different similarities index to known metabolites, most of which were previously characterized and found to have several antifungal activities against pathogens of agricultural and medical relevance revealed in Table S5. Each cluster consists of core biosynthetic genes, additional genes that predominantly code for metabolite tailoring enzymes, transport genes, regulatory genes, and other genes. Two NRPS-like siderophore clusters were predicted after the antiSMASH blast, in regions 27.1 and 11.1. For siderophore predicted on region 27.1, based on Pfam annotation contains 8 genes that contribute to siderophore biosynthesis, with two core genes belonging to luc A/C families involved in the biosynthesis of siderophore (Fig. 8 B), and a tailoring enzyme designated pyridoxal phosphate-dependent aminotransferase and a regulatory gene involved in transcriptional regulation. The predicted metabolites had a similarity index of 58%, with genes homolog to luc A/C siderophore biosynthesis protein in Streptomyces sp. T2 genes based on ClusterBlast (Fig. 8 C). The second siderophore is predicted on genome region 11.1 (Fig. 8 D) with 100% similar to desferrioxamine B &E of Streptomyces coelicolor A3 based on KnownClusterBlast analysis (Fig. 8 E) with a predicted structure revealed in Fig. 8 F. The gene overview revealed the presence of a core gene coding for luc A/C family siderophore biosynthetic protein, 10 genes coding for 10 tailoring enzymes, and 3 genes involved in transcriptional regulation. The luc A/C families of both regions based on Pfam annotation, are bound to ferric iron reductase transporter at the N terminal region. They function by catalyzing from the citrate, N epsilon-acetyl-N epsilon-hydroxylysine end, the discrete steps involved in aerobactin siderophore biosynthesis pathway. The genome region 9.1 and 34.1 had sequences similar to genes encoding hybrid clusters, which are a combination of two or more types of biosynthetic gene clusters. The genome region 9.1 (Fig. 9 A) contains predicted genes coding for three transcriptional regulators, type 1 polyketide synthase, and 4 core biosynthetic genes coding for amino acid adenylation protein domains involved in the synthesis of peptide antibiotics. The multi-domain enzymes of the protein domains catalyze the condensation reaction to form peptide bonds in NRP biosynthesis. The most similar antibiotic is polyoxypeptin derived from Streptomyces sp. BJ20 (Fig. 9 B&C) based on MIBIG clustering. Polyoxypeptin (Fig. 9 D) has three type 1 polyketide synthase and transcriptional regulator predicted domains in the modular structure. The type II PKS oligosaccharide hybrid (Fig. 9 E) is encoded by genes located at the 34.1 genome region and is 82% similar to genes coding for grincamycin (Fig. 9 F,G) from Streptomyces lusitanus . This region has five core genes, two transport genes, 6 transcriptional regulatory genes, and 18 tailoring additional genes. One of the core genes is the two proteins containing nucleotide disphospho-sugar binding domains with a Rossmann-like alpha/beta fold. This domain is common at the C-terminal of erythromycin biosynthesis protein from Saccharopolyspora erythraea . The five core genes also contain an activator-dependent family glycosyltransferase enzyme which catalyzes glycosylation reaction and two ketoacyl synthases which are found between N and C terminal domains based on Pfam annotation. The sequence at the 8.1 genome region was predicted to be 53% similar to the sequences of Streptomyces coelicolor A3, which code for hopene metabolite. This region has seven accessory tailoring enzymes, one transport protein, and three core biosynthetic gene clusters. The core clusters consist of presqualene diphosphate synthase HPnD that catalyzes a molecule of farnesyl diphosphate into presqualene diphosphate, a core squalene synthase HpnC that catalyzes presqualene diphosphate to form squalene. These reactions occur in a two step head-head condensation reaction coupled with NADPH-dependent reduction to squalene. The last core biosynthetic gene cluster (BGC) is an N-terminal domain-sited cluster that catalyzes the cyclization of squalene to hopene, based on Pfam and MIBIG annotation (Fig. 10 A-C). At 26.3 genome region, the nucleotide sequences range between 436718 to 460798 and are predicted to be 54% similar to the sequences of Streptomyces avermitilis , which code for carotenoid. The core BGCs encoded in this region are lycopene cyclase and phytoene synthase family, as well as 5 accessory tailoring enzymes involved in the carotenoid pathway. Based on Pfam prediction, the phytoene synthase family catalyzes the two head-head conversion of two molecules of geranylgeranyl diphosphate to prephytoene diphosphate, which rearranges to form phytoene. The latter is cyclized by the β-lycopene and epsilon cyclase protein (lycopene cyclase) to form carotenoid (Fig. 10 D-F). Additionally, at the 27.2 genome region, the sequences are located from 148191 to 170347, and predicted to be 100% percent similar to sequences in Streptomyces coelicolor A3 coding for geosmin biosynthesis. The biosynthetic core gene within this region encodes germacradienol/geosmin synthase Cyc2, with 3 transport proteins, one transcriptional regulatory gene, and 4 tailoring enzymes. Based on Pfam annotation, the core cluster is a C-terminal terpene synthase that cyclizes linear terpene with diverse isoprene units (Fig. 10 G-I). Lastly, at region 28.1 are nucleotide sequences starting from 327992 to 349077 with 100% similarity to albaflavenone metabolite encoded by the sequence of Streptomyces coelicolor A3. In this region are genes coding for a transcriptional regulatory protein, a regulatory factor protein, two tailoring enzymes, and a core BGC for epi-isozizaene synthase. The core enzyme is a terpene synthase 2-C terminal metal-binding protein that catalysis terpene synthesis (Fig. 10 J-L). The first RiPP-like lantipeptide encoding region is the 21.1 genome region (Fig. 11 A), which consists of a transport and core biosynthetic gene clusters starting from 254146 to 264361 nucleotide sequences length. The genes in this region are 42% similar to the clusters in Streptomyces viridochromogenes DSM 40736 coding for informatipeptin biosynthesis (Fig. 11 B). The core BGC, based on Pfam annotation, is serine or histidine binding protein in Streptomyces protease A & B (streptogrisin A & B) belonging to the family S2A. The 21.2 genome region (Fig. 11 G), which has 22658 nucleotide sequences, consists of three transcriptional factors, 3 transport proteins, 2 tailoring enzymes, and a core biosynthetic gene clusters coding for class III lanthionine synthetase LanKC. The genes in this region are 100% similar to SapB in Streptomyces coelicolor A3 (Fig. 11 H). Based on Pfam's predicted function, SapB (Fig. 11 I) is a protein kinase that drives the phosphorylation of tyrosine and threonine through the catalysis of the gamma phosphate from nucleotide triphosphates to one or more amino acid residues in the protein substrate side-chain, resulting to protein conformational and functional changes. The nucleotide sequence at genome region 26.1 has 24511 nucleotides, which are 4% similar to those of Micromonospora sp. B006 encoding class 1 lantipeptide (diazaquinomycin H & J-Figure 11F). This region encodes three transcriptional regulators, 3 tailoring enzymes, a transport protein, and two core BGCs. Based on Pfam annotation these BGCs are lanthionine-like, which are putatively created by the dehydration of serine and threonine to form a product coupled with cysteine. They are bacterial-derived ribosomally synthesized peptides that act as antimicrobials. The last RiPP-like lantipeptide predicted at genome region 27.3 with a total of 11326 nucleotide sequences (Fig. 11 C), consists of a core biosynthetic gene cluster encoding an uncharacterized protein (based on Pfam domain annotation), and a tailoring enzyme. At genome region 26.2 (Fig. 12 A) are genes coding for type III polyketide synthases, predicted to catalyze the condensation, chain elongation, and cyclization of alkyl chain, acyl chain, and malonyl CoA structure to form alkylresorcinol-the predicted known metabolites with a similarity index of 100% (Fig. 12 B). This region has two transcriptional regulators, 10 tailoring enzymes, two transport genes, and a core BGC. At genome region 33.1 (Fig. 12 C) are 21 tailoring enzymes (additional biosynthetic genes), a transport gene, 7 transcriptional regulatory genes, and two core biosynthetic genes coding for β-keto acyl (acyl-carrier protein) synthase family protein and ketosynthase. These genes are 83% similar to those of Streptomyces avermitilis known to code for spore pigment biosynthesis (Fig. 12 D). The two core BGCs-β-keto acyl synthases (N & C terminals) are type II polyketide synthases that catalyze the multiple condensation reaction involving the acyl chain elongation or malonyl CoA base, followed by cyclization and further modification to form spore pigment. Ectoine and butylactone are the last two BGCs predicted in Streptomyces sp. OP7. They are neither PKS nor NRPS. At genome region 5.1 are nucleotide sequences (Fig. 13 A) similar to those in Streptomyces anulatus coding for known ectoine (based on KnowClusterBlast) with MIBIG predicted structure in Fig. 13 C. The nucleotide sequences are 10399 in total encoding five tailoring enzymes and a core gene encoding ectoine synthase, which characterizes the cyclization of N-γ-acetyl-L-2,4-diaminobutyric (Nγ-ADABA) into ectoine based on Pfam annotation. The 9.2 genome region has 7039 nucleotide sequences (Fig. 13 D) coding for a known metabolite-butyrolactone (Fig. 13 E), with three regulatory genes, a single tailoring enzyme, and a core biosynthetic gene cluster. The latter codes for A-factor biosynthesis core enzyme essential for streptomycin production. A pangenome analysis was conducted to identify genes with shared or strain-specific functions using the closest three organisms from GTDB and TYGS taxonomic assignments with genome datasets at the public database repository as well as the genome of the strain Streptomyces sp. OP7. When analyzed using KBaseGenome Pangenome, Streptomyces sp. OP7 contains 503 genes with sequences not similar to any of the genes in the genome of the reference organisms (termed the singletons)—about 5880 genes (orthologs) from Streptomyces sp. OP7 is similar to the other reference strains, while about 2497, 3728, and 3981 homolog families are similar to Streptomyces ginkgonis , Streptomyces sp. DH-12 and Streptomyces tendae genomes (Table S6). In addition, the clade arrangement of the core fraction in the genome is shown in Fig. 8 , which reflects the specific core genes present in all but Streptomyces sp. OP7 and non-clade specific core are present in all genomes and partial pangenomes, i.e., genes present in more than one genome and fewer than all. Homolog family genes coding for the following biosynthetic gene clusters: Tcml family type II polyketide cyclase, type I polyketide, type I transferase domain protein, terpene cyclase, malto-oligosyltrehalose synthase, IucA/IucC family siderophore biosynthetic protein and ectoine synthase, were shared among all genomes of strains, hence reflecting a non-clade specific core. Meanwhile, tetratricopeptide repeat protein, siderophore interacting protein, pentapeptide repeat protein, and acyclic terpene utilization family protein were partially shared amongst all strains except Streptomyces ginkgonis , type A2 lantipepetide, SC00268 family class II lanthipeptide, and SapB/Amfs family lanthipeptide were shared in Streptomyces tendae and Streptomyces sp. OP7. However, it is worth noting that other genes encoded functions were similarly shared among strains, but based on the focus of this study, these are the biosynthetic gene clusters encoded and shared by the user and reference strains. Discussion The selection of plant protective strains from maize rhizosphere against the pathogens of maize is a strategic approach to plant protection (Babalola et al., 2022). Hence, some bacteria closely related to the host plant in the rhizosphere community reportedly play a significant role in plant health assurance. Additionally, native Actinomycetes adapted to the host plant and environmental conditions, which evolved in close association with the plant, and which are possibly specialized in producing metabolites that could play antagonistic functionalities are exploited (Chen et al., 2019, Wang et al., 2021). In this study, Actinomycetes strains, some of which appeared colour-heaped and chalky with a powdery and velvety surface as well as black to straw/yellowish reverse colouration were isolated from the rhizosphere of maize plants with the predominance of Streptomycetes. This prevalence could be due to their ability to survive under stress and adapt to extreme environments ranging from extreme pH to temperature under arid conditions. Most Actinomycetes strains proliferated under diverse pH ranges and were isolated from an arid region, with no pretreatment added. A similar study conducted by Binayke et al. (2018) revealed the abundance of 77% Actinomycetes, predominantly Streptomycetes, as the culturable bacterial population in arid and saline soil. In addition, two rare Actinomycetes were cultured in this study, Microbacterium sp. and Microbacterium binotti , which exhibited varying activity against the test pathogens. In a similar study conducted by Savi et al. (2019), Microbacterium sp. LGMB471-derived secondary metabolites displayed antifungal activity against citrus black spot disease caused by Phyllosticta citricarpa . A phylogenetic study conducted by Babalola et al. (2009), also reflects the dominance of Streptomycetes in an antarctic dry soil. The first maize pathogen isolated from North West field, coded N-B, which is identical to Bipolaris sp., with morphology similar to Bipolaris maydis reported by Manzar et al. (2022) resulted in foliar blight of Zea mays . The inoculation of maize leaves with this strain produced dot-like leaf spots. Then the strain Fusarium equiseti (S-W) induced leaf spot lesions when spray inoculated, a lesion similar to the strain source sampled from the field. On the contrary, Phoma sp. induced a cigar-like lesion (which was non blight-like) that extended into dry leaves on the 28th day but displayed the highest disease symptoms on the 21st day, which was quite different from the diseased leaves from the field. In a study conducted by Ramos Romero et al. (2021), Phoma spp. was characterized in Central Europe and observed to cause similar leaf lesions in leaf-treated samples. Maize leaf blight diseases have been reported to have a negative impact on crop productivity (Nsibo et al., 2019), and the application of a sustainable approach to assuage this problem is a step in the right direction in boosting production over time and possibly meeting the world food demand. The Actinomycetes displayed variable fungal antagonistic activity when applied as a whole cell and as a cell-free supernatant extract usage. As a whole cell, some strains displayed complete inhibition of the pathogens resulting in a clear zone by inhibiting fuzzy mycelial formation. On the other hand, the cell-free extract of the same isolate inhibited all pathogens. This revealed the possible fungal growth inhibitive functionalities of secondary metabolites that could be released more in the cell-free supernatant than in the whole cell. A study conducted by Mamphogoro et al. (2021) revealed the efficacy of bacterial antagonists against bacterial wilt disease of sweet pepper crops. A study conducted by Chukwuneme et al. (2021) and Olanrewaju and Babalola (2022) revealed the functionalities of Actinomycetes from plant rhizosphere in preventing fungal cell wall extension by degrading their macromolecular components and displaying antimicrobial activity. The most effective sanger-sequencing identified Actinomycetale bacterium strain, confirmed to be Streptomyces sp. through whole genome sequencing, as the most active against Fusarium equiseti and Bipolaris sp. Several prolific Streptomyces sp. have been applied for plant protection. In a study, Streptomyces sp. H4 was applied to antagonize the growth of Colletotrichum fragariae (Li et al., 2021), Streptomyces griseocarneus R132 was also used to inhibit the phytopathogens of pepper (Liotti et al., 2019). Therefore, Streptomyces spp. are prolific metabolite producers, which could assuage the negative crop production impact of maize leaf blight causative fungal diseases. Most predicted genes code for metabolites biosynthesis pathway metabolites in the clusters of this genus using antiSMASH tools consist of major polyketides, non-ribosomal peptides, and terpenes with diverse functionalities including plant protection (Antony et al., 2024). In this study, the genes identified predicted with KEGG housed blastKAOLA encode pathways for the biosynthesis of siderophore, streptogrisin, chitinase, mycolic acid, fatty acid, ectoine, phenazine known to have fungal pathogens control ability. Additionally, genes coding for nitrogen fixation, Fe-S biosynthesis, proline biosynthesis, quorum sensing, plant-pathogen interaction, and heat shock proteins were also annotated, which are involved in plant growth promotion and environmental adaptation. The biosynthetic gene clusters and their encoded most similar metabolites in the genome were confirmed using other webserver tools like Uniprot and NCBI. In addition, PRISM tool revealed some clusters with predicted metabolite scaffolds. Some of the antimicrobial metabolites predicted using the antiSMASH and PRISM include desferroxamine B & E, informatipeptin, lanthionine, diazaquinomycin H & J, albaflavenone, tetracenomycin, augucyclines, nosiheptide, frenolicin, griseoviridin, fijimycin A, tylosin, mithramycin, lactonamycin, lankacidin, lankamycin, virginiamycin and showdomycin. Similar compounds were encoded by Streptomyces sp. S29 isolated from the lupine rhizosphere active against Aspergillus niger and Botrytis cinerea and Streptomyces sp. VITGV100 obtained from tomato plant (Jarmusch et al., 2021, Veilumuthu et al., 2022). Furthermore, out of these listed metabolome predicted using antiSMASH and PRISM tools, it is worthy of note that confirmed antifungal metabolites by previous studies for plant protection include: desferroxamine B & E against Aspergillus niger and Botrytis cinerea , lanthionine was also produced by Streptomyces spp. against Fusarium oxysporum subsp. Fragariae causative agent of strawberry Fusarium wilt disease, tetracenomycin D obtained from Streptomyces canus reportedly inhibited rice blast causative Magnaporthe grisea , Frenolicin B obtained from Streptomyces sp. NEAU-H3 against Fusarium spp causing head blight in wheat (Braesel et al., 2019, Han et al., 2021). Other predicted metabolites reportedly displayed antimicrobial activity against clinical microbes except showdomycin-like metabolite which has been applied against kiwi bacterial canker (Huang et al., 2023). This Streptomyces strain selected could be used to produce metabolites with antifungal efficacy against maize-associated plant pathogens, which could help circumvent leaf blight diseases in the agricultural system. Conclusion This present study contributes significantly to unveiling diversities of active Actinomycetes species- Streptomyces albidoflavus, Streptomyces pilosus, Streptomyces erythrogriseus, Streptomyces harbinensis, Streptomyces albus, Microbacterium sp., Streptomyces parvulus, Streptomyces gancidicus, Streptomyces sp. and Microbacterium binottii (consisting of the Streptomycetes and rare Actinomycetes groups) present in maize rhizosphere, as well as their activity against maize-associated leaf blight disease causative fungi. In addition, it reveals the genome profiling of the most active Streptomyces sp. OP7 with a broad-spectrum of antagonistic activity against plant pathogens identified. Predicting intracellularly containing metabolites within the genome known to have antifungal functionalities against fungal pathogens. Hence, this strain could be applied as a potential bioinoculant and an antifungal compound-producing precursor to control maize crop-associated leaf blight diseases. Declarations Funding The National Research Foundation (NRF) of South Africa funded this study for grants that have supported research in the Microbial Biotechnology Laboratory. Ethical Approval This study does not contain any study with animals performed by any of the authors. Acknowledgment OOB's profound gratitude goes to the National Research Foundation (NRF) of South Africa for the grants (UID: 123634; UID132595) awarded to her. In addition, OPO would like to thank NRF for the stipend (UID: 138583) and North-West University for the bursary. The intellectual feedback provided by OOB (supervisor) and ASA (co-supervisor) regarding the manuscript is greatly appreciated, and the financial support provided by OOB. Authors and Affiliations Food Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North-West University, Private Bag X2046, Mmabatho, 2735, South Africa Oghoye Priscilla Oyedoh, Ayansina Segun Ayangbenro & Olubukola Oluranti Babalola Authors Contributions Conceptualization: Oghoye Priscilla Oyedoh & Olubukola Oluranti Babalola; Methodology: Oghoye Priscilla Oyedoh & Ayansina S. Ayangbenro, Formal analysis and investigation: Oghoye Priscilla Oyedoh; Writing—original draft preparation: Oghoye Priscilla Oyedoh; Writing—review and editing: Olubukola O. Babalola & Ayansina S. Ayangbenro; Funding acquisition: Olubukola O. Babalola; Resources: Olubukola O. Babalola; Supervision: Olubukola O. Babalola. Corresponding author Correspondence to Olubukola Oluranti Babalola Supplementary Materials Below is the link to the electronic supplementary material Supplementary file (2.4MB) Conflict Of Interest The authors reported no declarations of interest. 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The Environmental Food Crisis: The Environment's role in averting future food crises. In:A UNEP rapid response assessment. Arendal, Norway: United Nations Environment Programme/GRID-Arendal, pp104. Veilumuthu, P., Nagarajan, T., Sasikumar, S., Siva, R., Jose, S. & Christopher, J. G. 2022 Streptomyces sp. VITGV100: an endophyte from Lycopersicon esculentum as new source of indole type compounds. Bch Syst Eco 105:104523. DOI: https://doi.org/10.21203/rs.3.rs-791591/v1 Wang, P. P., Yang, L. F., Sun, J. L., Yang, Y., Qu, Y., Wang, C. X., Huang, L. Q., Cui, X. M. & Liu, Y. 2021 Structure and function of rhizosphere and root endophyte microbial communities associated with healthy and root rot diseased Panax notoginseng . https://doi.org/10.21203/rs.3.rs-478704/v1 Wang, S., Sun, L., Zhang, W., Chi, F., Hao, X., Bian, J. & Li, Y. 2020 Bacillus velezensis BM21, a potential and efficient biocontrol agent in control of corn stalk rot caused by Fusarium graminearum . Egy J Bio Pt Cont 30 :1-10. https://doi.org/10.1186/s41938-020-0209-6. Supplementary Files SupplementaryMaterialforBiologiaJournal.docx Cite Share Download PDF Status: Published Journal Publication published 22 May, 2025 Read the published version in Biologia → Version 1 posted Editorial decision: Major revisions 17 Dec, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers invited by journal 13 Nov, 2024 Editor assigned by journal 11 Nov, 2024 First submitted to journal 07 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5385478","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377726180,"identity":"21073a05-47e9-418b-90f5-88b3145b76fc","order_by":0,"name":"Oghoye Priscilla Oyedoh","email":"","orcid":"","institution":"North-West University Potchefstroom Campus: North-West University","correspondingAuthor":false,"prefix":"","firstName":"Oghoye","middleName":"Priscilla","lastName":"Oyedoh","suffix":""},{"id":377726181,"identity":"03141512-6dbe-475c-9e09-75eb224d4a19","order_by":1,"name":"Ayansina Segun Ayangbenro","email":"","orcid":"","institution":"North-West University Potchefstroom Campus: North-West University","correspondingAuthor":false,"prefix":"","firstName":"Ayansina","middleName":"Segun","lastName":"Ayangbenro","suffix":""},{"id":377726182,"identity":"7cc17b25-5abe-421f-a29a-d896dcfafcf1","order_by":2,"name":"Olubukola Oluranti Babalola","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0003-4344-1909","institution":"North-West University","correspondingAuthor":true,"prefix":"","firstName":"Olubukola","middleName":"Oluranti","lastName":"Babalola","suffix":""}],"badges":[],"createdAt":"2024-11-04 06:54:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5385478/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5385478/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11756-025-01948-x","type":"published","date":"2025-05-22T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70365472,"identity":"04c9f607-e3d0-4241-8a24-79e0a668a2b8","added_by":"auto","created_at":"2024-12-02 14:08:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":335861,"visible":true,"origin":"","legend":"\u003cp\u003eCultural and Morphological Characterization of Putative Leaf-blight causative fungal Isolates: (a) C-Z-M; front view-grey, reverse view-black (b) N-B; front view-black, reverse view-black (c) S-W; front view-fluffy cream, reverse view-brown\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/e5fba657fcc2bb5a1ef4fec8.png"},{"id":70363511,"identity":"eecba645-cfc2-4ecc-9e81-e1257c15d185","added_by":"auto","created_at":"2024-12-02 13:52:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":417248,"visible":true,"origin":"","legend":"\u003cp\u003eDisease manifestation of leaflets treated with fungal isolates and sterile distilled water\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/0e877a4930cf78e56047e8f4.png"},{"id":70364754,"identity":"af94ed9a-20f4-420e-9dd1-76e04381da62","added_by":"auto","created_at":"2024-12-02 14:00:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":702447,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary screening of Actinomycetes isolates against fungal isolates. A and b are active putative Actinomycetes (longitudinal) isolates against fungal pathogens (diagonal) while c is inactive\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/87252da7b9e97ea1581aaa00.png"},{"id":70363507,"identity":"3016515c-8e9c-4e74-ada0-bdf8c659f4d7","added_by":"auto","created_at":"2024-12-02 13:52:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":464106,"visible":true,"origin":"","legend":"\u003cp\u003eThe phylogenetic tree of all Actinomycetes strains obtained from maize rhizosphere with MEGA 11 constructed based on 16S rRNA sequence analysis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/e573e02a2294c3a85bce5648.png"},{"id":70365468,"identity":"dd10dfa5-1264-45f6-aba8-fc4fb4e89e3c","added_by":"auto","created_at":"2024-12-02 14:08:20","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23708,"visible":true,"origin":"","legend":"\u003cp\u003eThe phylogenetic tree of the fungal pathogens of maize crop with MEGA 11 constructed based on 16S rRNA sequence analysis (N-B-\u003cem\u003eBipolaris\u003c/em\u003e sp., C-Z-M-\u003cem\u003ePhoma\u003c/em\u003e sp., S-W-\u003cem\u003eFusarium equiseti\u003c/em\u003e)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/3d99e6ba6a558da2e545c6e9.png"},{"id":70366708,"identity":"1c8de127-3c32-4c71-91d6-817124093e11","added_by":"auto","created_at":"2024-12-02 14:16:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":96844,"visible":true,"origin":"","legend":"\u003cp\u003eRasttk SEED view for \u003cem\u003eStreptomyces\u003c/em\u003esp. OP7 with categories of subsystem and their feature counts\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/f7a14975d9a30df3552c0bfb.png"},{"id":70363517,"identity":"3d1afb35-a63f-4c3d-aa51-bd222a306a42","added_by":"auto","created_at":"2024-12-02 13:52:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":325453,"visible":true,"origin":"","legend":"\u003cp\u003eThe phylogenetic tree of the selected \u003cem\u003eStreptomyces\u003c/em\u003e sp. strain OP7 with the highest inhibitory\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/6ab18c8d7e1f329dfbf46c11.png"},{"id":70364757,"identity":"a51a4b56-a47b-4f34-8147-e63919ec8bb6","added_by":"auto","created_at":"2024-12-02 14:00:20","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":91663,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted NRPS-independent siderophore biosynthetic gene clusters\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/f250f968288a9759a1653f1b.png"},{"id":70363515,"identity":"288d4a95-44fc-41b9-a097-c5b5d7bcf03e","added_by":"auto","created_at":"2024-12-02 13:52:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":231688,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted hybrid biosynthetic gene clusters\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/f1e33fb45e81f48d9c5911c2.png"},{"id":70365473,"identity":"973bbf3b-8c1c-4492-8344-b2317bc39a0d","added_by":"auto","created_at":"2024-12-02 14:08:20","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":220691,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted terpene biosynthetic gene clusters\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/2a154685a6980ff83f80137e.png"},{"id":70364760,"identity":"3537ff2f-1b75-46f3-ad42-be3189e0a946","added_by":"auto","created_at":"2024-12-02 14:00:20","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":228426,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted peptide biosynthetic gene clusters\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/cdb32a0cec2e580f3e41534e.png"},{"id":70365471,"identity":"9a075c32-ae78-4bcc-9004-8a53020365d6","added_by":"auto","created_at":"2024-12-02 14:08:20","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":86673,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted PKS biosynthetic gene clusters\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/76cfdab024f1e9932b02bd5f.png"},{"id":70364759,"identity":"9ae5d634-3a04-44cc-af0c-e9b5eff24216","added_by":"auto","created_at":"2024-12-02 14:00:20","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":44620,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted ectoine and butyrolactone Biosynthetic gene clusters\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/466d816fa06122b953fe8412.png"},{"id":70363521,"identity":"543e4c44-8d62-4316-834a-e11526da4f8b","added_by":"auto","created_at":"2024-12-02 13:52:20","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":253354,"visible":true,"origin":"","legend":"\u003cp\u003ePangenome circle plot for \u003cem\u003eStreptomyces\u003c/em\u003esp. OP7 and its closely related strains\u003c/p\u003e","description":"","filename":"floatimage14.png","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/31a6d865350a1dc74d309412.png"},{"id":83459975,"identity":"eb550180-3edf-4656-b05f-d18cd19d4770","added_by":"auto","created_at":"2025-05-26 16:07:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4858871,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/86aa6c45-e19b-41e1-8ccd-c0605d136ab1.pdf"},{"id":70363519,"identity":"8c119b0d-174a-4a29-a613-c490890e7bf6","added_by":"auto","created_at":"2024-12-02 13:52:20","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2523429,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialforBiologiaJournal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5385478/v1/a53619fa7bb7ec9352212f8c.docx"}],"financialInterests":"","formattedTitle":"Actinomycetes Strain Selection From Maize Rhizosphere With Antagonistic Potential Against Fungal Phytopathogen","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMaize is the largest local field crop produced in South Africa, with a gross income value of about 1.94\u0026nbsp;billion US dollars as of 2018/2019 (Cowling, 2023). The delicate challenge of crop productivity in South Africa (due to climate shocks resulting in extreme weather conditions and high evapotranspiration) by 2080 has been projected to reduce cereal productivity by 15\u0026ndash;50%. In addition, this climate change will cause an evolutionary shift in the structure and function of microbial communities, causing most pathogens to be dominant, which could lead to diseases (UNEP et al., 2009, Nhemachena et al., 2020). \u003cem\u003ePhaeosphaeria\u003c/em\u003e leaf spot, common rust, northern leaf blight, and gray leaf spot have been reported as causative agents of high leaf blight disease pressure in smallholder farming environments in South Africa, which poses a threat to maize food security. Aside from the mentioned, 'Helminthosporiod groups' such as \u003cem\u003eBipolaris\u003c/em\u003e sp., \u003cem\u003eCurvularia\u003c/em\u003e spp., \u003cem\u003eDrechslera\u003c/em\u003e sp., and \u003cem\u003eExserohilum\u003c/em\u003e sp. and other conidia producing Ascomycetes like \u003cem\u003eFusarium\u003c/em\u003e and \u003cem\u003ePhoma\u003c/em\u003e spp. are known to cause leaf blight diseases in maize and cereal crops (Nsibo et al., 2019, Berger et al., 2020, Craven et al., 2020). Some of these pathogens have been reported to cause maize blight with diseases commonly controlled in crops using resistant cultivars or synthetic chemicals. While through rural development programs, these resistant cultivars could be freely disbursed to farmers, an extra input production strategy that involves precise and timely management such as weeding, fertilizer application, and pesticide application is accompanied by its application, which is not cost-effective. In addition, the alternative application of synthetic chemicals could be a non-sustainable, polluting manner of enhancing resistant phytopathogenic fungal communities in the field, which affects public health and the surroundings (Alipour Kafi et al., 2021). To assist farmholders in achieving a profit margin, a sustainable mode of phytopathogen elimination has to be adopted during maize production.\u003c/p\u003e \u003cp\u003ePrevious meta-studies of bacterial structure and function revealed the dominance of Actinomycetes in disease-suppressive rhizosphere soil and leaf blight-infected maize, owed to their ability to portray antagonistic functionality through metabolite production. A 10X meta-study accompanied by metabolites prediction conducted by Tracanna et al. (2021) using antiSMASH (the antibiotics and secondary metabolites analysis shell) tools revealed some antifungal metabolites in a root soil suppressed by \u003cem\u003eFusarium culmorum\u003c/em\u003e, with some metabolites been associated with Streptomycetes bacteria. In addition, the structural and functional profiling of microbial diversity in plant environments revealed Actinomycetes and metabolites prevalence, including enzymes, volatile organic compounds, hydrogen cyanide, organic acids, antibiotics, and siderophores (Babalola et al., 2022). This suggests the luxuriant availability of this phylum in the rhizosphere of plants and their possible role in disease management. Hence, a possible strategy is selecting a native Actinomycetes strain adapted to maize environmental conditions (the rhizosphere).\u003c/p\u003e \u003cp\u003eThe rhizosphere of plants is the soil area beneath the root, which experiences constant root exudates, soil, and microbial interactions for plant health. The beneficial microbes involved in plant health assurance include bacteria, fungi, and archaea in a particular order of abundance. Bacteria assure plant health against diseases through metabolite secretion, particularly antibiotics, to directly antagonize pathogen proliferation (Adegboye and Babalola, 2013, Adedeji and Babalola, 2020). In addition, numerous studies have revealed the inherent hub of gene clusters in Actinomycetes, which are conserved coding regions for known and unknown metabolites of various biotechnological applications, especially in the agricultural sector (Ch\u0026aacute;vez-Avila et al., 2023, Oyedoh et al., 2023, Thirugnanam et al., 2023). These metabolites are categorized based on their biosynthetic gene clusters, which are polyketides, non-ribosomal peptides, lasso peptides, saccharides, aminoglycosides, terpenoids, and ribosomally synthesized and post-translationally modified peptides RIPPs (ribosomally synthesized and post-translationally modified peptides) (Veilumuthu et al., 2022). These biosynthetic genes are clustered in the genome and have modular domains or enzymes that function in an assembly line to produce complex biomolecules. Therefore, one could predict the underlying mechanism of disease inhibition through the metabolites harboured with antifungal properties (Tracanna et al., 2021). This study is designed to identify the biosynthetic gene clusters through genome mining after selecting a potential antagonistic strain from the rhizosphere of maize plants.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSampling Collection, Preparation and Isolation\u003c/h2\u003e \u003cp\u003eMaize plant rhizosphere-soil samples were collected from the School of Agriculture Research Farm, North-West University in Molelwane, Mahikeng, North West Province, South Africa. An arid environment affected by drought, with a coordinate of 25\u003csup\u003eO\u003c/sup\u003e47\u003csup\u003eI\u003c/sup\u003e26.5\u003csup\u003eII\u003c/sup\u003eN25\u003csup\u003eO\u003c/sup\u003e37\u003csup\u003eI\u003c/sup\u003e04.1\u003csup\u003eII\u003c/sup\u003eE and stored at -20\u003csup\u003eo\u003c/sup\u003eC. In addition, symptomatic leaves were randomly collected from a maize-producing area at the farm at the vegetative stage of growth. The samples were placed in sterile containers, and transported to the laboratory for processing. Diverse seed cultivars (Wema 3128, BG685R5, Pan 6479, Pan 5R-ZBH6, and Pan 5R-2CP8) were also collected from the university farm for planting.\u003c/p\u003e \u003cp\u003eTo reduce the growth of fast-growing bacteria in the samples before culturing, the rhizosphere soil samples were physically, chemically, and synergistically pretreated. Pretreatment was conducted physically by heating at 100\u003csup\u003eO\u003c/sup\u003eC for 1hr, chemically diluting in 9ml of 1.5% phenol solution, and synergistically combining both treatments (Ayoib et al., 2023). In contrast, the non-pretreated samples served as the control.\u003c/p\u003e \u003cp\u003eThe pretreated aliquot was plated on modified Actinomycetes isolation agar (AIA) prepared with five mM phosphate buffer at neutral, acidic, and alkaline pH and supplemented with cycloheximide/nalidixic acid to isolate wider Actinomycetes diversity. Pure and discrete colonies of Actinomycetes isolates were putatively identified using morphological (Ayoib et al., 2023) and selective biochemical tests such as motility, catalase, hydrogen utilization, indole, and oxidase tests. Actinomycetes-like isolates were stored at -80\u003csup\u003eO\u003c/sup\u003eC temperature with glycerol (20%).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePathogen Isolation, Identification, and\u003c/b\u003e \u003cb\u003eInvitro\u003c/b\u003e \u003cb\u003ePathogenicity Test\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePrior to the pathogenicity test, maize seeds (Wema 3128, BG685R5, Pan 6479, Pan 5R-ZBH6, and Pan 5R-2CP8 cultivars) were collected from the University farm. They were confirmed viable by conducting viability and pre-germination tests. The seeds were soaked in 500ml sterile distilled water, placed in a beaker, and troubled with hands. Then, the seeds above the water were discarded, while those that sank below the water were used for a pre-germination test. Before the pre-germination test, the seeds were surface sterilized in 1% hypochlorite and then rinsed with sterile distilled water thrice until the chemical was removed. The seeds were air-dried under sterile conditions in a laminar flow cabinet (Filta-Matix Laminar Flow Cabinets). Then 50 seeds were plated on a paper towel infused with 10ml water in sterile Petri dishes (10 seeds per plate). The Petri dishes were covered with another 10ml infused paper towel and incubated at 30\u003csup\u003eO\u003c/sup\u003eC for seven days. After this, the percentage of germination was determined using the formula described below:\u003c/p\u003e \u003cp\u003ePercentage Seed Germination (%) = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\raisebox{1ex}{$\\text{n}$}\\!\\left/\\:\\!\\raisebox{-1ex}{$\\text{N}$}\\right.\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere n\u0026thinsp;=\u0026thinsp;number of germinated seeds after seven days, N\u0026thinsp;=\u0026thinsp;total of seeds\u003c/p\u003e \u003cp\u003eIn sterile bags, diseased maize leaves showing symptoms of early blight at the vegetative stage of growth were collected from the university farm in Molelwane and processed within a few hours after sampling.\u003c/p\u003e \u003cp\u003eSymptomatic maize leave (WEMA 3128 cultivar) cut into 2cm pieces were sterilized with 1% sodium hypochlorite for 1mins, washed thrice in sterile distilled water, then dried on sterilized filter paper and plated on a potato dextrose agar plate containing 0.6ml of 0.5mg/l chloramphenicol. The fungal colonies growing around each bit were assessed (this isolation was conducted in triplicate to confirm culture obtained) and sub-cultured. Three isolates were repeatedly obtained from all symptomatic leaves and stained with lactophenol cotton blue, and viewed under a photographed microscope with 40X magnification based on cultural (shape, size, colour \u0026amp; texture) and morphological characteristics (shape, size, and colour of conidia).\u003c/p\u003e \u003cp\u003eThe soil was sieved with a 2mm diameter filter and weighed before sterilization at 121\u003csup\u003eO\u003c/sup\u003eC for 15mins. The soil was moistened to a maximum retention capacity (MRC- the maximum water quantity the soil can absorb without being flooded) of 2/9th substrate water for 18hrs before sowing. Then five seeds were sown per pot in 5cm depth and 2cm apart, the seed holes were closed, and pots were kept in the greenhouse. The pots were maintained at 48hrs intervals by watering, and after ten days, the least vigor plant per pot was removed. Under greenhouse conditions, the average day and night temperatures were 25\u003csup\u003eO\u003c/sup\u003eC and 18\u003csup\u003eO\u003c/sup\u003eC. Then foliar treatment was carried out by spraying the abaxial surface of leaves with a spore suspension of pathogens 10\u003csup\u003e5\u003c/sup\u003e spores/ml. At the same time, sterile water was used as control. The spraying was done at the 5\u0026ndash;6 leaf stage, 20ml of conidia suspension was evenly sprayed and the pots were kept humid for 18hrs (by covering them with polythene bags). After 18hrs, the plants were removed from the humid environment and kept in a greenhouse. The inoculated and non-inoculated foliage were examined, and then compared; from the leaves which exhibited symptoms, the organism was re-isolated and the culture obtained was compared with the original isolates to confirm the identity based on Koch's postulate. Disease severity was assessed at 7-day intervals by using a 1\u0026ndash;5 disease rating scale, where 1\u0026thinsp;=\u0026thinsp;no symptoms; 2\u0026thinsp;=\u0026thinsp;moderate lesion development below the leaves; 3\u0026thinsp;=\u0026thinsp;heavy lesion development on and below the leaf with a few lesions above it; 4\u0026thinsp;=\u0026thinsp;severe lesion development on all but the uppermost leaves, which may have a few lesions; and 5\u0026thinsp;=\u0026thinsp;all leaves dead (Aregbesola et al., 2020). After which the mean of the severity ratings was used to assess the virulence of each isolate, and the experiment was conducted twice, in duplicate.\u003c/p\u003e \u003cp\u003eAlso, the disease incidence was determined, which is \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{n}}{\\text{N}}\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWhere n\u0026thinsp;=\u0026thinsp;number of diseased plants, N\u0026thinsp;=\u0026thinsp;total number of plants\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary and Secondary Screening\u003c/h3\u003e\n\u003cp\u003eAll putative isolates were subjected to primary screening described by Jing et al. (2020), with some modifications. Briefly, yeast-malt agar (prepared with yeast, malt, glucose, and agar-agar) plates were streaked with Actinomycetes isolates longitudinally and incubated for two days. Then, the selected maize leaf blight causative fungal isolates were diagonally streaked across the Actinomycetes isolates to determine the inhibitory effects of Actinomycetes whole cell against fungal isolates, and isolates with activity were selected for further screening. The zone of inhibition was measured in cm for the isolates that displayed a zone of clearance. However, some isolates that did not display a zone of clearance but inhibited mycelium formation were designated A- for active, AA for more active, and AAA for most active.\u003c/p\u003e \u003cp\u003eThis screening was conducted to determine the inhibitory effect of supernatant cell-free extract obtained from active isolates from previous screening against the test pathogens. Briefly, the supernatant extract from each Actinomycetes isolate, after centrifugation at 12,000rpm for 10mins at 4\u003csup\u003eO\u003c/sup\u003eC, was then filtrated by syringe Millipore filter 0.22\u0026micro;m. Antifungal activity of the cell-free filtrated supernatant against the isolated plant pathogen was evaluated using the food poisoning techniques and mycelial growth rate method described by Wang et al. (2020). Briefly, the supernatant extract (25ml) and potato dextrose agar (75ml) were mixed (after cooling the agar to 45\u003csup\u003eO\u003c/sup\u003eC) in sterile Petri dishes and allowed to solidify. Then 0.6cm of each fungal isolate was transferred into the center of solidified PDA plates supplemented with supernatant in duplicates. PDA plates inoculated without supernatant extracts served as control and all plates were incubated at 25\u003csup\u003eO\u003c/sup\u003eC until control plates were completely grown. After incubation, fungal mycelial growth was observed and expressed as percentage growth inhibition as described below.\u003c/p\u003e \u003cp\u003eInhibitive % = (control diameter \u0026ndash;treated diameter/control diameter) X 100\u003c/p\u003e\n\u003ch3\u003eMolecular Identification of Selected Active Bacterial and Fungal Isolates\u003c/h3\u003e\n\u003cp\u003eBased on their antifungal activity against the phytopathogens, 14 out of 23 Actinomycetes isolates were chosen for molecular analysis. Then, three phytopathogens manifesting diverse levels of disease severity were also identified. Actinomycetes and fungal DNA were extracted using the Quick-DNA\u0026trade; Miniprep Kit, particularly for bacteria and fungi, manufactured by Zymo Research, Irvine, CA, USA, following the manufacturer's protocol. The purity of the DNA was determined using the Thermo Fischer Scientific, CA, USA-manufactured NanoDrop spectrophotometer. The pure DNA was amplified using 25\u0026micro;l reaction mixture containing 2\u0026micro;l of template DNA, 2\u0026micro;l of primer (specific primers of Actinomycetes 27F (5'-AGAGTTTGATCCTGGCTCAG-3') (Bruce et al., 1992) and 16Sact1114R (5'-GAGTTGACCCCGGCRGT-3') (Martina et al., 2008)), 8.5\u0026micro;l nuclease-free water and 12.5\u0026micro;l OneTaq Quick-Load 2* master mix with standard buffer (New England Biolabs NEB) in a PCR (polymerase chain reaction) of pre-denaturation at 95\u003csup\u003eO\u003c/sup\u003eC for 2mins, denaturation at 95 \u003csup\u003eO\u003c/sup\u003eC for 30secs, annealing at 53 \u003csup\u003eO\u003c/sup\u003eC for 30secs, extension at 72 \u003csup\u003eO\u003c/sup\u003eC for 1min and final extension at 72\u003csup\u003eO\u003c/sup\u003eC for 7mins in 30cycles. The selected phytopathogens were identified using 12.5\u0026micro;l master mix, 2\u0026micro;l DNA, 9.5\u0026micro;l nuclease-free water and 1\u0026micro;l primer (ITS1 (5\u0026prime;-TCCGTAGGTGAACCTGCGG-3\u0026prime;) and ITS4 (5\u0026prime; TCCTCCGCTTATTGATATGC-3\u0026prime;)) in a PCR analysis involving denaturation at 95\u003csup\u003eO\u003c/sup\u003eC for 5mins, extension at 95\u003csup\u003eO\u003c/sup\u003eC for 30secs, annealing at 50\u003csup\u003eO\u003c/sup\u003eC for 45secs, elongation at 75\u003csup\u003eO\u003c/sup\u003eC for 1min and final elongation at 72\u003csup\u003eO\u003c/sup\u003eC at 10mins (Dacc\u0026ograve; et al., 2020). The PCR amplicon's purity was certified through electrophoresis using 1.5% (w/v) agarose gel. The PCR products were purified following the manufacturer's protocol on the ExoSAP-IT (Applied Biosystems, Foster City, CA, United States) kits. The pure amplicons were sequenced at Inquaba Biotec Ltd, Pretoria, South Africa. The sequenced 16S rRNA genes were compared with the sequences of identified organisms on the NCBI database. The phylogenetic trees were constructed using a neighbor-joining method of MEGA 6.0 (Kumar et al., 2018), and the distance of evolution was calculated using the maximum-parsimony algorithm and using the bootstrap analysis on 1000 replicates level of confidence.\u003c/p\u003e \u003cp\u003eFollowing the standard Illumina sequencing method at Novogene, Singapore, the whole genome sequencing of the rhizospheric Actinomycetes isolate M12 was performed on Illumina Novoseq 6000 platform. After FastQC quality sequence assessment, low-quality sequences and adaptors were trimmed with the trimmomatic software (Bolger et al., 2014). The genomic sequences of high-quality reads were analyzed on the KBase platform (Arkin et al., 2018). The processed sequences were further assembled with the Spade (Nurk et al., 2013), the assembled genome was annotated using RASTtk software servers (Allen et al., 2017), and functional annotation was done using BlastKOALA software housed by KEGG, all with default settings. Making use of recently updated features, taxonomic analysis was conducted for the whole genome data using GTDB and typed strain genome server (TYGS) by comparing the user's genome against GTDB database and ten typed strains genome, respectively (Meier-Kolthoff and G\u0026ouml;ker, 2019, Parks et al., 2022). The pan-genome of the user\u0026rsquo;s strain was analyzed against the most similar reference genome with full genome data on the NCBI RefSeq bank. Then, to predict the biosynthetic gene clusters of the annotated genes, antiSMASH 3.0 was used, and PRISM 4.0 detected the structural scaffold (Blin et al., 2023, Skinnider et al., 2020). Finally, the gene clusters detected with antiSMASH 3.0 were blasted with NCBI and UniProt software to confirm the cluster.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn this study, 58 Actinomycetes-like isolates were isolated from healthy maize rhizosphere obtained from the sampling location. Most of these isolates were derived from non-pretreated samples at pH 7 and 10, with two isolates coded M2 and M1 obtained from chemically pretreated samples at pH 10. The isolates were subjected to microscopic and selected biochemical characterization, and about 23 isolates were confirmed to be Actinomycetes (Table S1 and Figure S1). However, for the test isolates obtained from diseased leaves based on their macroscopic and microscopic analyses, the first fungal isolate was fluffy cream with a reverse brownish colour and slender, longitudinal conidia (Figure 1c); the second isolate is black front and reverse with short, oval, single, conidia morphology as shown in Figure 1b, while the last isolate on Figure 1a has glittering-like gray front with black reverse colour and smaller, single ovoid morphology but with denser conidiophores.\u003c/p\u003e \u003cp\u003eThen, to confirm the pathogenicity of the test isolates, the most viable maize seeds among the cultivars collected from the University Farm, which is Wema 3128, with a 100% germination rate (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), were planted in a greenhouse.\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\u003ePre-germination test\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\u003eSeed Cultivars\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrepared Seeds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCovered Seeds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGerminated Seeds\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage Germination (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWema 3128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBG685R5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePan 6479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePan 5R-ZBH6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePan 5R-2CP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\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\u003eThe \u003cem\u003eFusarium\u003c/em\u003e-treated leaves (S-W), displayed coalescence of lesions after 7 days of treatment and had the highest disease severity. For \u003cem\u003eBipolaris\u003c/em\u003e (N-B) treated leaves, a pin-hole-like lesion appeared within three days at the bottom leaflets and spread upward, then expanded into an elongated strip that transformed into chlorotic leaves after 14 days. The \u003cem\u003eBipolaris\u003c/em\u003e treated leaves had a high disease percentage incidence of about 80% after seven days of treatment, as indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For \u003cem\u003ePhoma\u003c/em\u003e-treated (C-Z-M) leaves a brown-like ellipsoid lesion appeared on the leaves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with a high disease incidence of 73.33%, which becomes longer after seven days and then transforms into chlorotic leaves after the 14th day. After 21 days of treatment, the lower leaflets were already showing chlorotic symptoms, which began from the elongated strip lesion area and the edges of the leaf. Leaves sprayed with \u003cem\u003eFusarium equiseti\u003c/em\u003e and \u003cem\u003eBipolaris\u003c/em\u003e sp. showed leaf blight-like lesions than leaves sprayed with \u003cem\u003ePhoma\u003c/em\u003e sp., while the control revealed no sign of disease. However, further experiments were conducted with the two leaf blight causative strains and \u003cem\u003ePhoma\u003c/em\u003e sp., based on the fact that the last has been reported as a maize pathogen.\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\u003e\u003cem\u003eInvivo\u003c/em\u003e pathogenicity confirmatory test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsolate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003ePercentage disease incidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eDisease Severity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.00\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.5\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Z-M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.75\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.98\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e56.67\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.08\u003csup\u003ec\u003c/sup\u003e\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\u003evalues followed by different lowercase letters within a column are significantly different according to the least significant difference test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll putative Actinomycetes isolates were subjected to an initial primary screening (Table S2) against the two leaf-blight causative maize pathogens and \u003cem\u003ePhoma\u003c/em\u003e sp. (on the basis that \u003cem\u003ePhoma\u003c/em\u003e sp. is a reported pathogen of other crops of economic benefits). Here, Actinomycetes whole cells with high antagonistic activity include M1, M6, M4, M2, M7, and M12. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea \u0026amp; b show the antagonistic activity of M12 and M6 streaked longitudinally against the three pathogens streaked diagonally, reflecting whole-cell growth of the Actinomycetes isolates into the pathogens, hence antagonizing the pathogen's growth, except for Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec that displays Actinomycetes inactivity against the pathogens.\u003c/p\u003e \u003cp\u003eFor secondary screening (Figure S2) with 50% extract, there was no significant difference between Actinomycetes isolate coded M12 and M14, but with 25%, only M12 remains constantly active against the pathogens, inhibiting pathogenic growth at a rate greater than 80% (for 50% extract); hence it was selected for whole genome sequencing, while M6 was more active against \u003cem\u003ePhoma\u003c/em\u003e sp. at both concentration.\u003c/p\u003e \u003cp\u003eAside from isolate M12 identified as \u003cem\u003eActinomycetales bacterium\u003c/em\u003e, which displayed the highest inhibitory level against the pathogens at both concentrations with accession number PP564474, isolate M2, M4, M5, M6, M7, and M14, M13, M16, and M17, inhibited the growth of the pathogens at diverse degree shown in Table S5 bearing accession numbers PP564468, PP564469, PP564470, PP564471, PP564472, PP564473, PP564475, PP564476 and PP56447 respectively. In addition, strain M1 could not be given an accession number due to its low percentage homology of 81% to its closest reference bacteria, \u003cem\u003eStrep\u003c/em\u003etomyces \u003cem\u003eerythrogriseus\u003c/em\u003e. In addition, the test pathogens C-Z-M, N-B, and S-W were identified as \u003cem\u003ePhoma\u003c/em\u003e sp. \u003cem\u003eBipolaris\u003c/em\u003e sp. and \u003cem\u003eFusarium equiseti\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with accession numbers PP563710, PP563711 and PP563712, respectively.\u003c/p\u003e \u003cp\u003eThe assembled genome of the most active Actinomycetes isolate-M12 was annotated with Rast pipelines and had a genome size of 7109246, 37 contigs, 59222 ORFs composed of scaffolds with an N50 of 563188bp and a DNA GC content of 72.4%. The genome encoded 6624 coding regions, 66 tRNA, 64 mRNA regions, and 6452 (97.4%) predicted genes with 192 functional roles. In addition, the subsystem categories in the annotated genome and their corresponding feature numbers are highlighted in Table S4. Based on the Rasttk annotation, the subsystem functions revealed the presence of 24-iron acquisition and metabolism proteins, 337-amino acid and derivative, 61-stress response, 110-protein metabolism, 17-nitrogen metabolism, 7-potassium metabolism, 7-sulphur metabolism, 295-carbohydrate metabolism, 126-isoprenoid and fatty acid 24-regulation and cell signaling, 13-dormancy and sporulation functionalities. Additionally, using the KEGG-housed BlastKaola software, the functional annotation, pathway, and molecular function identifier also annotated 2431 coding regions (38.7%), with 4193 (63.3%) been an uncharacterized or hypothetical proteins. The genes predicted include \u003cem\u003efep\u003c/em\u003e (D, G, C); \u003cem\u003efag\u003c/em\u003e (A, B, C), \u003cem\u003ecch\u003c/em\u003e (C, D, E), \u003cem\u003eact\u003c/em\u003e I, \u003cem\u003eakn\u003c/em\u003e (B, C, D) \u003cem\u003eent\u003c/em\u003e D, \u003cem\u003edhb\u003c/em\u003e F,and \u003cem\u003edes\u003c/em\u003e (H, G, F) genes coding for siderophore biosynthesis; \u003cem\u003espr\u003c/em\u003e (B, D) for streptogrisin biosynthesis; \u003cem\u003ecpb\u003c/em\u003e (D, E) for chitinase biosynthesis; \u003cem\u003eacpS\u003c/em\u003e involved in mycolic acid biosynthesis; \u003cem\u003efab\u003c/em\u003e F for fatty acid biosynthesis; \u003cem\u003eect\u003c/em\u003e (A, B, C, D) for ectoine biosynthesis; \u003cem\u003ephz\u003c/em\u003e E for phenazine biosynthesis; \u003cem\u003ekor\u003c/em\u003e B, \u003cem\u003ehem\u003c/em\u003e (C, B), \u003cem\u003ehis\u003c/em\u003e C, \u003cem\u003ecob\u003c/em\u003e A, \u003cem\u003ehem\u003c/em\u003e D, \u003cem\u003ethr\u003c/em\u003e (B, C), \u003cem\u003emqn\u003c/em\u003e (E, C), \u003cem\u003ecyo\u003c/em\u003e E, \u003cem\u003ecta\u003c/em\u003e B, \u003cem\u003edap\u003c/em\u003e A, \u003cem\u003efab\u003c/em\u003e G, \u003cem\u003eacp\u003c/em\u003e (P, M) for secondary metabolites biosynthesis. Other genes characterized aid in nitrogen fixations (\u003cem\u003enif\u003c/em\u003e U, \u003cem\u003enas\u003c/em\u003e A, B, \u003cem\u003enir\u003c/em\u003e B, D, \u003cem\u003enpd\u003c/em\u003e, \u003cem\u003encd\u003c/em\u003e and \u003cem\u003enar\u003c/em\u003e K); Fe-sulphur biosynthesis (\u003cem\u003esuf\u003c/em\u003e B,C,D,S, \u003cem\u003edap\u003c/em\u003e A, D); proline biosynthesis (\u003cem\u003epub\u003c/em\u003e B, \u003cem\u003epro\u003c/em\u003e DH); heat shock proteins (\u003cem\u003ehtp\u003c/em\u003e X); plant-pathogen interaction (\u003cem\u003eTuf\u003c/em\u003e, \u003cem\u003ehtp\u003c/em\u003e G); thermogenesis (\u003cem\u003ecta\u003c/em\u003e B, \u003cem\u003ecyo\u003c/em\u003e E) and quorum sensing (\u003cem\u003esec\u003c/em\u003e E, \u003cem\u003eddp\u003c/em\u003e FD). The genome study of the most active isolate M12 revealed that the strain is related to its closest homolog, \u003cem\u003eStreptomyces\u003c/em\u003e sp. DH-12, with an ANI of 95.96 and \u003cem\u003eStreptomyces tendae\u003c/em\u003e; therefore, the isolate was identified a\u003cem\u003es Streptomyces\u003c/em\u003e sp., as represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The phylogenetic relationship based on the full-length genome sequencing conducted with type strains genome server (TYGS) revealed closest similarities with \u003cem\u003eStreptomyces matensis\u003c/em\u003e JCM 4277 with a digital DNA-DNA hybridization value of 40.4%, which is less than 70% and delineates possibly new species (based on TYGS only).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe annotated genome of \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7 contains 16 biosynthetic gene clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) with different similarities index to known metabolites, most of which were previously characterized and found to have several antifungal activities against pathogens of agricultural and medical relevance revealed in Table S5. Each cluster consists of core biosynthetic genes, additional genes that predominantly code for metabolite tailoring enzymes, transport genes, regulatory genes, and other genes.\u003c/p\u003e \u003cp\u003eTwo NRPS-like siderophore clusters were predicted after the antiSMASH blast, in regions 27.1 and 11.1. For siderophore predicted on region 27.1, based on Pfam annotation contains 8 genes that contribute to siderophore biosynthesis, with two core genes belonging to \u003cem\u003eluc\u003c/em\u003eA/C families involved in the biosynthesis of siderophore (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), and a tailoring enzyme designated pyridoxal phosphate-dependent aminotransferase and a regulatory gene involved in transcriptional regulation. The predicted metabolites had a similarity index of 58%, with genes homolog to \u003cem\u003eluc\u003c/em\u003eA/C siderophore biosynthesis protein in \u003cem\u003eStreptomyces\u003c/em\u003e sp. T2 genes based on ClusterBlast (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). The second siderophore is predicted on genome region 11.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD) with 100% similar to desferrioxamine B \u0026amp;E of \u003cem\u003eStreptomyces coelicolor\u003c/em\u003e A3 based on KnownClusterBlast analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE) with a predicted structure revealed in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF. The gene overview revealed the presence of a core gene coding for \u003cem\u003eluc\u003c/em\u003eA/C family siderophore biosynthetic protein, 10 genes coding for 10 tailoring enzymes, and 3 genes involved in transcriptional regulation. The \u003cem\u003eluc\u003c/em\u003eA/C families of both regions based on Pfam annotation, are bound to ferric iron reductase transporter at the N terminal region. They function by catalyzing from the citrate, N epsilon-acetyl-N epsilon-hydroxylysine end, the discrete steps involved in aerobactin siderophore biosynthesis pathway.\u003c/p\u003e \u003cp\u003eThe genome region 9.1 and 34.1 had sequences similar to genes encoding hybrid clusters, which are a combination of two or more types of biosynthetic gene clusters. The genome region 9.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA) contains predicted genes coding for three transcriptional regulators, type 1 polyketide synthase, and 4 core biosynthetic genes coding for amino acid adenylation protein domains involved in the synthesis of peptide antibiotics. The multi-domain enzymes of the protein domains catalyze the condensation reaction to form peptide bonds in NRP biosynthesis. The most similar antibiotic is polyoxypeptin derived from \u003cem\u003eStreptomyces\u003c/em\u003e sp. BJ20 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB\u0026amp;C) based on MIBIG clustering. Polyoxypeptin (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD) has three type 1 polyketide synthase and transcriptional regulator predicted domains in the modular structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe type II PKS oligosaccharide hybrid (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE) is encoded by genes located at the 34.1 genome region and is 82% similar to genes coding for grincamycin (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF,G) from \u003cem\u003eStreptomyces lusitanus\u003c/em\u003e. This region has five core genes, two transport genes, 6 transcriptional regulatory genes, and 18 tailoring additional genes. One of the core genes is the two proteins containing nucleotide disphospho-sugar binding domains with a Rossmann-like alpha/beta fold. This domain is common at the C-terminal of erythromycin biosynthesis protein from \u003cem\u003eSaccharopolyspora erythraea\u003c/em\u003e. The five core genes also contain an activator-dependent family glycosyltransferase enzyme which catalyzes glycosylation reaction and two ketoacyl synthases which are found between N and C terminal domains based on Pfam annotation.\u003c/p\u003e \u003cp\u003eThe sequence at the 8.1 genome region was predicted to be 53% similar to the sequences of \u003cem\u003eStreptomyces coelicolor\u003c/em\u003e A3, which code for hopene metabolite. This region has seven accessory tailoring enzymes, one transport protein, and three core biosynthetic gene clusters. The core clusters consist of presqualene diphosphate synthase HPnD that catalyzes a molecule of farnesyl diphosphate into presqualene diphosphate, a core squalene synthase HpnC that catalyzes presqualene diphosphate to form squalene. These reactions occur in a two step head-head condensation reaction coupled with NADPH-dependent reduction to squalene. The last core biosynthetic gene cluster (BGC) is an N-terminal domain-sited cluster that catalyzes the cyclization of squalene to hopene, based on Pfam and MIBIG annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-C). At 26.3 genome region, the nucleotide sequences range between 436718 to 460798 and are predicted to be 54% similar to the sequences of \u003cem\u003eStreptomyces avermitilis\u003c/em\u003e, which code for carotenoid. The core BGCs encoded in this region are lycopene cyclase and phytoene synthase family, as well as 5 accessory tailoring enzymes involved in the carotenoid pathway. Based on Pfam prediction, the phytoene synthase family catalyzes the two head-head conversion of two molecules of geranylgeranyl diphosphate to prephytoene diphosphate, which rearranges to form phytoene. The latter is cyclized by the β-lycopene and epsilon cyclase protein (lycopene cyclase) to form carotenoid (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD-F). Additionally, at the 27.2 genome region, the sequences are located from 148191 to 170347, and predicted to be 100% percent similar to sequences in \u003cem\u003eStreptomyces coelicolor\u003c/em\u003e A3 coding for geosmin biosynthesis. The biosynthetic core gene within this region encodes germacradienol/geosmin synthase Cyc2, with 3 transport proteins, one transcriptional regulatory gene, and 4 tailoring enzymes. Based on Pfam annotation, the core cluster is a C-terminal terpene synthase that cyclizes linear terpene with diverse isoprene units (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eG-I). Lastly, at region 28.1 are nucleotide sequences starting from 327992 to 349077 with 100% similarity to albaflavenone metabolite encoded by the sequence of \u003cem\u003eStreptomyces coelicolor\u003c/em\u003e A3. In this region are genes coding for a transcriptional regulatory protein, a regulatory factor protein, two tailoring enzymes, and a core BGC for epi-isozizaene synthase. The core enzyme is a terpene synthase 2-C terminal metal-binding protein that catalysis terpene synthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eJ-L).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe first RiPP-like lantipeptide encoding region is the 21.1 genome region (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA), which consists of a transport and core biosynthetic gene clusters starting from 254146 to 264361 nucleotide sequences length. The genes in this region are 42% similar to the clusters in \u003cem\u003eStreptomyces viridochromogenes\u003c/em\u003e DSM 40736 coding for informatipeptin biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). The core BGC, based on Pfam annotation, is serine or histidine binding protein in \u003cem\u003eStreptomyces\u003c/em\u003e protease A \u0026amp; B (streptogrisin A \u0026amp; B) belonging to the family S2A. The 21.2 genome region (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eG), which has 22658 nucleotide sequences, consists of three transcriptional factors, 3 transport proteins, 2 tailoring enzymes, and a core biosynthetic gene clusters coding for class III lanthionine synthetase LanKC. The genes in this region are 100% similar to \u003cem\u003eSapB\u003c/em\u003e in \u003cem\u003eStreptomyces coelicolor\u003c/em\u003e A3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eH). Based on Pfam's predicted function, \u003cem\u003eSapB\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eI) is a protein kinase that drives the phosphorylation of tyrosine and threonine through the catalysis of the gamma phosphate from nucleotide triphosphates to one or more amino acid residues in the protein substrate side-chain, resulting to protein conformational and functional changes. The nucleotide sequence at genome region 26.1 has 24511 nucleotides, which are 4% similar to those of \u003cem\u003eMicromonospora\u003c/em\u003e sp. B006 encoding class 1 lantipeptide (diazaquinomycin H \u0026amp; J-Figure 11F). This region encodes three transcriptional regulators, 3 tailoring enzymes, a transport protein, and two core BGCs. Based on Pfam annotation these BGCs are lanthionine-like, which are putatively created by the dehydration of serine and threonine to form a product coupled with cysteine. They are bacterial-derived ribosomally synthesized peptides that act as antimicrobials. The last RiPP-like lantipeptide predicted at genome region 27.3 with a total of 11326 nucleotide sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC), consists of a core biosynthetic gene cluster encoding an uncharacterized protein (based on Pfam domain annotation), and a tailoring enzyme.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt genome region 26.2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA) are genes coding for type III polyketide synthases, predicted to catalyze the condensation, chain elongation, and cyclization of alkyl chain, acyl chain, and malonyl CoA structure to form alkylresorcinol-the predicted known metabolites with a similarity index of 100% (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eB). This region has two transcriptional regulators, 10 tailoring enzymes, two transport genes, and a core BGC. At genome region 33.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eC) are 21 tailoring enzymes (additional biosynthetic genes), a transport gene, 7 transcriptional regulatory genes, and two core biosynthetic genes coding for β-keto acyl (acyl-carrier protein) synthase family protein and ketosynthase. These genes are 83% similar to those of \u003cem\u003eStreptomyces avermitilis\u003c/em\u003e known to code for spore pigment biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eD). The two core BGCs-β-keto acyl synthases (N \u0026amp; C terminals) are type II polyketide synthases that catalyze the multiple condensation reaction involving the acyl chain elongation or malonyl CoA base, followed by cyclization and further modification to form spore pigment.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEctoine and butylactone are the last two BGCs predicted in \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7. They are neither PKS nor NRPS. At genome region 5.1 are nucleotide sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eA) similar to those in \u003cem\u003eStreptomyces anulatus\u003c/em\u003e coding for known ectoine (based on KnowClusterBlast) with MIBIG predicted structure in Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eC. The nucleotide sequences are 10399 in total encoding five tailoring enzymes and a core gene encoding ectoine synthase, which characterizes the cyclization of N-γ-acetyl-L-2,4-diaminobutyric (Nγ-ADABA) into ectoine based on Pfam annotation. The 9.2 genome region has 7039 nucleotide sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eD) coding for a known metabolite-butyrolactone (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003eE), with three regulatory genes, a single tailoring enzyme, and a core biosynthetic gene cluster. The latter codes for A-factor biosynthesis core enzyme essential for streptomycin production.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA pangenome analysis was conducted to identify genes with shared or strain-specific functions using the closest three organisms from GTDB and TYGS taxonomic assignments with genome datasets at the public database repository as well as the genome of the strain \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7. When analyzed using KBaseGenome Pangenome, \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7 contains 503 genes with sequences not similar to any of the genes in the genome of the reference organisms (termed the singletons)\u0026mdash;about 5880 genes (orthologs) from \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7 is similar to the other reference strains, while about 2497, 3728, and 3981 homolog families are similar to \u003cem\u003eStreptomyces ginkgonis\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e sp. DH-12 and \u003cem\u003eStreptomyces tendae\u003c/em\u003e genomes (Table S6). In addition, the clade arrangement of the core fraction in the genome is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, which reflects the specific core genes present in all but \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7 and non-clade specific core are present in all genomes and partial pangenomes, i.e., genes present in more than one genome and fewer than all. Homolog family genes coding for the following biosynthetic gene clusters: Tcml family type II polyketide cyclase, type I polyketide, type I transferase domain protein, terpene cyclase, malto-oligosyltrehalose synthase, IucA/IucC family siderophore biosynthetic protein and ectoine synthase, were shared among all genomes of strains, hence reflecting a non-clade specific core. Meanwhile, tetratricopeptide repeat protein, siderophore interacting protein, pentapeptide repeat protein, and acyclic terpene utilization family protein were partially shared amongst all strains except \u003cem\u003eStreptomyces ginkgonis\u003c/em\u003e, type A2 lantipepetide, SC00268 family class II lanthipeptide, and SapB/Amfs family lanthipeptide were shared in \u003cem\u003eStreptomyces tendae\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7. However, it is worth noting that other genes encoded functions were similarly shared among strains, but based on the focus of this study, these are the biosynthetic gene clusters encoded and shared by the user and reference strains.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe selection of plant protective strains from maize rhizosphere against the pathogens of maize is a strategic approach to plant protection (Babalola et al., 2022). Hence, some bacteria closely related to the host plant in the rhizosphere community reportedly play a significant role in plant health assurance. Additionally, native Actinomycetes adapted to the host plant and environmental conditions, which evolved in close association with the plant, and which are possibly specialized in producing metabolites that could play antagonistic functionalities are exploited (Chen et al., 2019, Wang et al., 2021).\u003c/p\u003e \u003cp\u003eIn this study, Actinomycetes strains, some of which appeared colour-heaped and chalky with a powdery and velvety surface as well as black to straw/yellowish reverse colouration were isolated from the rhizosphere of maize plants with the predominance of Streptomycetes. This prevalence could be due to their ability to survive under stress and adapt to extreme environments ranging from extreme pH to temperature under arid conditions. Most Actinomycetes strains proliferated under diverse pH ranges and were isolated from an arid region, with no pretreatment added. A similar study conducted by Binayke et al. (2018) revealed the abundance of 77% Actinomycetes, predominantly Streptomycetes, as the culturable bacterial population in arid and saline soil. In addition, two rare Actinomycetes were cultured in this study, \u003cem\u003eMicrobacterium\u003c/em\u003e sp. and \u003cem\u003eMicrobacterium binotti\u003c/em\u003e, which exhibited varying activity against the test pathogens. In a similar study conducted by Savi et al. (2019), \u003cem\u003eMicrobacterium\u003c/em\u003e sp. LGMB471-derived secondary metabolites displayed antifungal activity against citrus black spot disease caused by \u003cem\u003ePhyllosticta citricarpa\u003c/em\u003e. A phylogenetic study conducted by Babalola et al. (2009), also reflects the dominance of Streptomycetes in an antarctic dry soil.\u003c/p\u003e \u003cp\u003eThe first maize pathogen isolated from North West field, coded N-B, which is identical to \u003cem\u003eBipolaris\u003c/em\u003e sp., with morphology similar to \u003cem\u003eBipolaris maydis\u003c/em\u003e reported by Manzar et al. (2022) resulted in foliar blight of \u003cem\u003eZea mays\u003c/em\u003e. The inoculation of maize leaves with this strain produced dot-like leaf spots. Then the strain \u003cem\u003eFusarium equiseti\u003c/em\u003e (S-W) induced leaf spot lesions when spray inoculated, a lesion similar to the strain source sampled from the field. On the contrary, \u003cem\u003ePhoma\u003c/em\u003e sp. induced a cigar-like lesion (which was non blight-like) that extended into dry leaves on the 28th day but displayed the highest disease symptoms on the 21st day, which was quite different from the diseased leaves from the field. In a study conducted by Ramos Romero et al. (2021), \u003cem\u003ePhoma\u003c/em\u003e spp. was characterized in Central Europe and observed to cause similar leaf lesions in leaf-treated samples. Maize leaf blight diseases have been reported to have a negative impact on crop productivity (Nsibo et al., 2019), and the application of a sustainable approach to assuage this problem is a step in the right direction in boosting production over time and possibly meeting the world food demand.\u003c/p\u003e \u003cp\u003eThe Actinomycetes displayed variable fungal antagonistic activity when applied as a whole cell and as a cell-free supernatant extract usage. As a whole cell, some strains displayed complete inhibition of the pathogens resulting in a clear zone by inhibiting fuzzy mycelial formation. On the other hand, the cell-free extract of the same isolate inhibited all pathogens. This revealed the possible fungal growth inhibitive functionalities of secondary metabolites that could be released more in the cell-free supernatant than in the whole cell. A study conducted by Mamphogoro et al. (2021) revealed the efficacy of bacterial antagonists against bacterial wilt disease of sweet pepper crops. A study conducted by Chukwuneme et al. (2021) and Olanrewaju and Babalola (2022) revealed the functionalities of Actinomycetes from plant rhizosphere in preventing fungal cell wall extension by degrading their macromolecular components and displaying antimicrobial activity. The most effective sanger-sequencing identified \u003cem\u003eActinomycetale bacterium\u003c/em\u003e strain, confirmed to be \u003cem\u003eStreptomyces\u003c/em\u003e sp. through whole genome sequencing, as the most active against \u003cem\u003eFusarium equiseti\u003c/em\u003e and \u003cem\u003eBipolaris\u003c/em\u003e sp. Several prolific \u003cem\u003eStreptomyces\u003c/em\u003e sp. have been applied for plant protection. In a study, \u003cem\u003eStreptomyces\u003c/em\u003e sp. H4 was applied to antagonize the growth of \u003cem\u003eColletotrichum fragariae\u003c/em\u003e (Li et al., 2021), \u003cem\u003eStreptomyces griseocarneus\u003c/em\u003e R132 was also used to inhibit the phytopathogens of pepper (Liotti et al., 2019). Therefore, Streptomyces spp. are prolific metabolite producers, which could assuage the negative crop production impact of maize leaf blight causative fungal diseases. Most predicted genes code for metabolites biosynthesis pathway metabolites in the clusters of this genus using antiSMASH tools consist of major polyketides, non-ribosomal peptides, and terpenes with diverse functionalities including plant protection (Antony et al., 2024). In this study, the genes identified predicted with KEGG housed blastKAOLA encode pathways for the biosynthesis of siderophore, streptogrisin, chitinase, mycolic acid, fatty acid, ectoine, phenazine known to have fungal pathogens control ability. Additionally, genes coding for nitrogen fixation, Fe-S biosynthesis, proline biosynthesis, quorum sensing, plant-pathogen interaction, and heat shock proteins were also annotated, which are involved in plant growth promotion and environmental adaptation. The biosynthetic gene clusters and their encoded most similar metabolites in the genome were confirmed using other webserver tools like Uniprot and NCBI. In addition, PRISM tool revealed some clusters with predicted metabolite scaffolds. Some of the antimicrobial metabolites predicted using the antiSMASH and PRISM include desferroxamine B \u0026amp; E, informatipeptin, lanthionine, diazaquinomycin H \u0026amp; J, albaflavenone, tetracenomycin, augucyclines, nosiheptide, frenolicin, griseoviridin, fijimycin A, tylosin, mithramycin, lactonamycin, lankacidin, lankamycin, virginiamycin and showdomycin. Similar compounds were encoded by \u003cem\u003eStreptomyces\u003c/em\u003e sp. S29 isolated from the lupine rhizosphere active against \u003cem\u003eAspergillus niger\u003c/em\u003e and \u003cem\u003eBotrytis cinerea\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e sp. VITGV100 obtained from tomato plant (Jarmusch et al., 2021, Veilumuthu et al., 2022). Furthermore, out of these listed metabolome predicted using antiSMASH and PRISM tools, it is worthy of note that confirmed antifungal metabolites by previous studies for plant protection include: desferroxamine B \u0026amp; E against \u003cem\u003eAspergillus niger\u003c/em\u003e and \u003cem\u003eBotrytis cinerea\u003c/em\u003e, lanthionine was also produced by \u003cem\u003eStreptomyces\u003c/em\u003e spp. against \u003cem\u003eFusarium oxysporum\u003c/em\u003e subsp. Fragariae causative agent of strawberry \u003cem\u003eFusarium\u003c/em\u003e wilt disease, tetracenomycin D obtained from \u003cem\u003eStreptomyces canus\u003c/em\u003e reportedly inhibited rice blast causative \u003cem\u003eMagnaporthe grisea\u003c/em\u003e, Frenolicin B obtained from \u003cem\u003eStreptomyces\u003c/em\u003e sp. NEAU-H3 against \u003cem\u003eFusarium\u003c/em\u003e spp causing head blight in wheat (Braesel et al., 2019, Han et al., 2021). Other predicted metabolites reportedly displayed antimicrobial activity against clinical microbes except showdomycin-like metabolite which has been applied against kiwi bacterial canker (Huang et al., 2023).\u003c/p\u003e \u003cp\u003eThis \u003cem\u003eStreptomyces\u003c/em\u003e strain selected could be used to produce metabolites with antifungal efficacy against maize-associated plant pathogens, which could help circumvent leaf blight diseases in the agricultural system.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis present study contributes significantly to unveiling diversities of active Actinomycetes species- Streptomyces albidoflavus, Streptomyces pilosus, Streptomyces erythrogriseus, Streptomyces harbinensis, Streptomyces albus, Microbacterium sp., Streptomyces parvulus, Streptomyces gancidicus, Streptomyces sp. and Microbacterium binottii (consisting of the Streptomycetes and rare Actinomycetes groups) present in maize rhizosphere, as well as their activity against maize-associated leaf blight disease causative fungi. In addition, it reveals the genome profiling of the most active Streptomyces sp. OP7 with a broad-spectrum of antagonistic activity against plant pathogens identified. Predicting intracellularly containing metabolites within the genome known to have antifungal functionalities against fungal pathogens. Hence, this strain could be applied as a potential bioinoculant and an antifungal compound-producing precursor to control maize crop-associated leaf blight diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe National Research Foundation (NRF) of South Africa funded this study for grants that have supported research in the Microbial Biotechnology Laboratory.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not contain any study with animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOOB\u0026apos;s profound gratitude goes to the National Research Foundation (NRF) of South Africa for the grants (UID: 123634; UID132595) awarded to her. In addition, OPO would like to thank NRF for the stipend (UID: 138583) and North-West University for the bursary. The intellectual feedback provided by OOB (supervisor) and ASA (co-supervisor) regarding the manuscript is greatly appreciated, and the financial support provided by OOB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFood Security and Safety Focus Area, Faculty of Natural and Agricultural Sciences, North-West University, Private Bag X2046, Mmabatho, 2735, South Africa\u003c/p\u003e\n\u003cp\u003eOghoye Priscilla Oyedoh, Ayansina Segun Ayangbenro \u0026amp; Olubukola Oluranti Babalola\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Oghoye Priscilla Oyedoh \u0026amp; Olubukola Oluranti Babalola; Methodology: Oghoye Priscilla Oyedoh \u0026amp; Ayansina S. Ayangbenro, Formal analysis and investigation: Oghoye Priscilla Oyedoh; Writing\u0026mdash;original draft preparation: Oghoye Priscilla Oyedoh; Writing\u0026mdash;review and editing: Olubukola O. Babalola \u0026amp; Ayansina S. Ayangbenro; Funding acquisition: Olubukola O. Babalola; Resources: Olubukola O. Babalola; Supervision: Olubukola O. Babalola.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Olubukola Oluranti Babalola\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBelow is the link to the electronic supplementary material\u003c/p\u003e\n\u003cp\u003eSupplementary file\u0026nbsp;(2.4MB)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict Of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors reported no declarations of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdedeji, A. A. \u0026amp; Babalola, O. O. 2020 Secondary metabolites as plant defensive strategy: a large role for small molecules in the near root region. Planta 252:61. 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Egy J Bio Pt Cont\u003cem\u003e \u003c/em\u003e30 :1-10. https://doi.org/10.1186/s41938-020-0209-6. \u003c/li\u003e\n\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Maize, leaf blight, Streptomyces sp., secondary metabolites, antifungal activity","lastPublishedDoi":"10.21203/rs.3.rs-5385478/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5385478/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFungal infestation in maize reduces productivity by 80%, with leaf blight disease causing about 60% reduction in grain yield. Numerous studies have shown the efficacy of synthetic chemicals in reducing the disease severity in agro-systems, which was efficient but with several negative impacts. Hence, there is an urgency to search for a more sustainable alternative with similar or better efficiency. This study was conceptualized to select a strain with \u003cem\u003ein vitro\u003c/em\u003e antagonistic activity against leaf blight causative fungi and predict the secondary metabolites produced through the culture-dependent method and whole genome sequencing approach. Maize pathogens, \u003cem\u003eBipolaris\u003c/em\u003e sp., \u003cem\u003eFusarium equiseti\u003c/em\u003e, and \u003cem\u003ePhoma\u003c/em\u003e sp., were obtained from symptomatic leaves and known to cause leaf blight diseases in maize crops, and antagonized by \u003cem\u003eStreptomyces\u003c/em\u003e sp. OP7. The OP7 strain was isolated from the rhizosphere of maize crop and its cell-free supernatant extract showed antifungal activity against phytopathogens tested. The complete whole genome data of Streptomyces sp. OP7 revealed the presence of 16 biosynthetic gene clusters similar to metabolites with antifungal functional annotations implicating \u003cem\u003eStrep\u003c/em\u003etomyces sp. OP7\u0026rsquo;s capacity to produce valuable agroactive compounds.\u003c/p\u003e","manuscriptTitle":"Actinomycetes Strain Selection From Maize Rhizosphere With Antagonistic Potential Against Fungal Phytopathogen","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 13:52:15","doi":"10.21203/rs.3.rs-5385478/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2024-12-17T09:08:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-13T15:58:44+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-13T14:39:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-11T11:15:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biologia","date":"2024-11-08T03:08:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"biol","sideBox":"Learn more about [Biologia](http://link.springer.com/journal/11756)","snPcode":"11756","submissionUrl":"https://www.editorialmanager.com/biol/default2.aspx","title":"Biologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"36916f49-08da-4b61-ad96-7ff11761c59f","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:00:02+00:00","versionOfRecord":{"articleIdentity":"rs-5385478","link":"https://doi.org/10.1007/s11756-025-01948-x","journal":{"identity":"biologia","isVorOnly":false,"title":"Biologia"},"publishedOn":"2025-05-22 15:57:09","publishedOnDateReadable":"May 22nd, 2025"},"versionCreatedAt":"2024-12-02 13:52:15","video":"","vorDoi":"10.1007/s11756-025-01948-x","vorDoiUrl":"https://doi.org/10.1007/s11756-025-01948-x","workflowStages":[]},"version":"v1","identity":"rs-5385478","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5385478","identity":"rs-5385478","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}