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Benign Bacillus: decoding the genetic potential of native rhizosphere Bacillus spp. from rice, to induce plant growth and defense

  • # Authors contributed equally: Kalyani M. Barbadikar, Neha Attal

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  • Three bacterial strains namely Bacillus velezensis Strain BIK2, B. cabrialesii Strain BIK3, and B. paralicheniformis Strain BIK4, were extracted from indigenous rice soils in India. These strains demonstrated potent efficacy against major pathogens while stimulating plant growth in rice. Their genomic analysis indicated a rich array of genes associated with secondary metabolite production, plant growth promotion, elicitation, and biocontrol activities. Biosynthetic gene clusters having different classes of secondary metabolites surfactin, macrolactinH, bacillaene, fengycin, difficidin, bacillibactin, and bacilysin were identified using various online tools. This information may be used as template for identification of novel bioactive antibacterial, antifungal, and organic plant growth-promoting compounds. Toxin-antitoxin gene pairs identified could play roles in their antibiotic resistance and prevention of harmful deletions in the bacterial genomes. CRISPRs detected in these isolates offer prospects for future gene editing and patenting endeavors. Notably, the genomic profiles of BIK2, BIK3, and BIK4 underscore their emphasis on plant growth enhancement, evident through the presence of genes facilitating nitrogen fixation, phosphate, and potassium solubilization, and siderophore production. This comprehensive genomic insight paves the way for tailored Bacillus strains, facilitating the industrial production of efficacious biomolecules for enhancing plant growth, controlling pathogens, and advancing pharmacological applications.
  • Agricultural management practices impact soil physicochemical properties to a remarkable extent. Degradation of soil health has led to a contraction in agricultural production and soil biodiversity particularly due to conventional farming practices, indiscriminate use of inorganic fertilizers (INF) and inadequate input of residues[1]. Organic or inorganic fertilizers have been regarded as a critical component of agriculture to accomplish global food security goals[2]. The exogenous supply of fertilizers could easily alter soil properties by restoring the nutrients that have been absorbed by the plants[2]. Thus, implementing adequate nutrient management strategies could boost plant yield and sustain plant health. Tillage affects the soil, especially for crop production and consequently affects the agro-ecosystem functions. This involves the mechanical manipulation of the soil to modify soil attributes like soil water retention, evapotranspiration, and infiltration processes for better crop production. Thus, tillage practices coupled with fertilizer inputs may prove a viable strategy to improve soil health components such as nutrient status, biodiversity, and organic carbon.

    Soil serves as a major reservoir of nutrients for sustainable crop production. Intensive cultivation due to growing population burden has led to the decline of soil nutrient status that has adversely affected agricultural production. Various researchers have assessed the soil nutrient budget and the reasons behind decline of nutrient content in soil[3]. Soil management strategies have assisted in overcoming this problem to a greater extent. Tillage practices redistribute soil fertility and improve plant available nutrient content due to soil perturbations[4]. Different tillage and fertilization practices alter soil nutrient cycling over time[5]. Fertilization is an important agricultural practice which is known to increase nutrient availability in soil as well as plants[6]. A report has been compiled by Srivastava et al.[7], which assessed the effectiveness of different fertilizers on soil nutrient status in Indian soils.

    Soil biota has a vital role in the self-regulating functions of the soil to maintain soil quality which might reduce the reliance on anthropogenic activities. Soil microbial activities are sensitive to slight modifications in soil properties and could be used as an index of soil health[8]. Maintenance of microbial activity is essential for soil resilience as they influence a diverse range of parameters and activities including soil structure formation, soil SOM degradation, bio-geochemical cycling of nutrients etc.[9]. Various researchers have identified microbial parameters like microbial biomass carbon (MBC), potentially mineralizable nitrogen (PMN), soil respiration, microbial biomass nitrogen (MBN), and earthworm population as potential predictors of soil quality. Geisseler & Scow[10] have compiled a review on the affirmative influence of long-term mineral fertilization on the soil microbial community.

    Being the largest terrestrial carbon (C) reservoir, soil organic carbon (SOC) plays a significant role in agricultural productivity, soil quality, and climate change mitigation[11]. Manure addition, either solely or along with INF augments SOC content which helps in the maintenance and restoration of SOM more effectively as compared to the addition of INF alone[12]. Enhancement of recalcitrant and labile pools of SOC could be obtained through long-term manure application accentuating the necessity of continuous organic amendments for building up C and maintaining its stability[13]. Generally, compared with manure addition, INF application is relatively less capable of raising SOC and its labile fractions[14]. Alteration in SOC content because of management strategies and/or degradation or restoring processes is more prominent in the labile fraction of soil C[15]. Several fractions of soil C play vital roles in food web and nutrient cycles in soils besides influencing many biological properties of soil[16]. Thus, monitoring the response of SOC and its fractions to various management practices is of utmost importance.

    A positive impact on SOC under manure application coupled with INF in rice-wheat systems has been reported, as compared to sole applications of INFs[17]. Although ploughing and other mechanical disturbances in intensive farming cause rapid OM breakdown and SOC loss[18], additional carbon input into the soil through manure addition and rational fertilization increases carbon content[13]. Wei et al.[19] in light sandy loam soil of China found that the inclusion of crop straw together with inorganic N, P and K fertilizers showed better results for improving soil fertility over sole use of inorganic fertilizers. Zhu et al.[20] studied the influence of soil C through wheat straw, farmyard manure (FYM), green manure, and rice straw on plant growth, yield, and various soil properties and found that the recycling of SOM under intensive cultivation is completely reliant on net OM input and biomass inclusion. However, most of the studies on residue management and organically managed systems could not provide clear views regarding the relations between the quality of OM inputs and biological responses towards it. The disintegration of soil aggregates due to ploughing, use of heavy machinery, and residue removal has been reported widely under conventional tillage (CT) practices[21]. On the contrary, improvement in SOC stabilization has also been observed by some scientists[22]. Under CT, the disintegration of macro-aggregates into micro-aggregates is a prominent phenomenon, while conservation tillage has been identified as a useful practice for increasing macro-aggregates as well as carbon sequestration in agricultural soils[23]. By and large, the ploughing depth (0–20 cm) is taken into consideration for evaluating the impact of tillage and straw retention on soil aggregation[24], while degradation in deeper layers of soil is becoming a major constraint towards soil quality together with crop yield[25].

    Hence, the present review would be useful in determining how tillage practices and inorganic and organic fertilization impact nutrient availability in the soil, microbial composition and SOC fractions besides stocks under different land uses.

    Agricultural production is greatly influenced by nutrient availability and thus nutrient management is required for sustaining higher yields of crop. The term 'nutrient availability' refers to the quantity of nutrients in chemical forms accessible to plant roots or compounds likely to be converted to such forms throughout the growing season in lieu of the total amount of nutrients in the soil. For optimum growth, different crops require specifically designed nutrient ratios. Plants need macronutrients [nitrogen (N), phosphorus (P), potassium (K) in higher concentrations], secondary nutrients [calcium (Ca), magnesium (Mg), sulphur (S) in moderate amounts as compared to macronutrients] and Micronutrients [Zn (zinc), Fe (iron), Cu (copper), B (boron), Mn (manganese), Mo (molybdenum) in smaller amounts] for sustainable growth and production[26]. Fertilizers assist the monitoring of soil nutrient levels by direct addition of required nutrients into the soil through different sources and tillage practices may alter the concentration of available plant nutrients through soil perturbations. Various studies on the influence of fertilization and tillage practices on available plant nutrients have been discussed below.

    Yue et al.[27] reported that long-term fertilization through manure/INF improved the macronutrient content of Ultisol soil in China. Two doses of NPK (2NPK) considerably improved soil properties over a single dose (NPK). Combined application (NPK + OM) resulted in higher hydrolysable N and available P over the sole OM application. The total K content was higher under the treatments NPK, 2NPK and NPK + OM than sole OM treatment, whereas available K was higher in treatments NPK + OM and 2NPK over the sole OM and NPK. Likewise, OM, INF, and OM + INF were evaluated for their potential to regulate the soil macronutrient dynamics. Organic manure significantly improved the soil N content, whereas INF showed comparable results to that of the control treatment. Besides, all the treatments improved available P and exchangeable K concentration[28].

    Hasnain et al.[29] performed comparative studies of different ratios of INF + compost and different application times for the chemical N fertilizer on silty loamy soils of China. The available nitrogen and phosphorous content were greater in conjoint OM + INF application over the bare INF and control application irrespective of N application time. Soil quality substantially improved with increasing ratio of compost and 70:30 (INF to compost ratio) was found to be most suitable to maintain soil fertility and nutrient status. Another study by Liu et al.[30] reported the superior effects of NPK + pig manure and NPK + straw to improve soil available P and K over the control and sole NPK treatments. However, total N concentration did not exhibit any significant variation under any treatment.

    Shang et al.[31] accounted the positive impact of vermicompost and mushroom residue application on grassland soil fertility in China. The addition of organic manures improved available P and K content to a considerable extent. Under moisture-deficit winter wheat-fallow rotation, another study quantified the influence of residue management approaches and fertilizer rate on nutrient accrual. Residue burning resulted in no decline in soil macronutrient content, whereas the perpetual addition of FYM for 84 years significantly improved total N and extractable K and P concentration. Thus, residue incorporation along with FYM application may prove beneficial in reducing the temporal macronutrient decline[32].

    Ge et al.[33] examined the effects of NPK and NPK along with manure (NPKM) addition on the macronutrient status of Hapli-Udic Cambisol soil. The NPKM application resulted in the highest increase in total N, available-P and K concentration as compared to NPK and control. Likewise, mineral fertilization reduction and partial substitution with organic amendments have posed a significant influence on soil macronutrient status. Soil available P and K decreased after INF reduction[34]. Chen et al.[35] evinced that integrated application of manure and mineral fertilizers to red clay soil (typical Ultisols) improved hydrolyzed nitrogen and available P due to an increase in the decomposition of organic matter (OM) and N bio-fixation than sole mineral fertilizers and control.

    A long-term experiment was carried out out by Shiwakoti et al.[36] to ascertain the influence of N fertilization and tillage on macronutrient dynamics in soil. Nitrogen fertilization produced higher crop biomass which might have improved total N and P concentration in soil. Moreover, the reduced interaction between soil colloids and residue or greater cation exchange sites due to tillage practices could have augmented K concentration in 0−10 cm soil depth. Likewise, among tillage systems combined organic (poultry manure) and inorganic (lime and fertilizers) fertilization, no-tillage, and reduced tillage with organic fertilization resulted in higher availability of P owing to minimal disturbance of soil which decreases contact surface between phosphate ions and adsorption sites. Greater losses of K in runoff water under NT resulted in lower K availability under NT than CT[37].

    The influence of tillage systems on soil nutrient dynamics showed that minimal soil disturbances under zero tillage prohibited redistribution of soil nutrients and resulted in the highest available N, P, and K in the surface soil[38]. The influence of tillage timing on soil macronutrient status has also been assessed under tillage treatments that are fall tillage (FT), spring tillage (ST), no tillage (NT), and disk/chisel tillage (DT/CT) on mixed mesic Typic Haploxerolls soil. All the tillage systems differed in the quantity of residues generated. Thus, variation in the decomposition of crop residue and mineralization of SOM resulted in variable rates of nutrient release. The FT and ST had the highest N content over DT/CT and NT systems at corresponding depth. The N content also decreased with soil depth irrespective of tillage treatment. The available P and extractable K were highest under NT at the top 10 cm soil depth and increased over time[39]. Residue management in combination with tillage treatments (ST and CT) has been reported to affect the soil macronutrient status in Bangladesh. Tillage treatments enhanced the total N content to a considerable extent. Moreover, 3 years of residue retention led to a higher concentration of total N, available P and K in the soil.

    The combinations of N, P, and K in different ratios together with two rates of organic fertilizer (OF) applied on the aquic Inceptisol having sandy loam texture influenced the micronutrient status of the soil[40]. Soil Zn content decreased with time when no fertilizer was applied as compared to organic fertilizer (OF) application. The mineral fertilizer treatments led to a substantial increase in DTPA-extractable micronutrients in the soil. The higher micronutrient concentration due to higher OM highlights the importance of maintaining OM for soil fertility and higher crop production. Further studies revealed that long-term application of sole N fertilizers led to a significant decline in total Zn and Cu, whereas Mn and Fe status improved through atmospheric deposition. Phosphorus and OF addition along with straw incorporation markedly increased total Zn, Cu, Fe, and Mn. The DTPA-extractable Mn, Zn, Fe, and Cu were also higher in OF treatment, thus demonstrating the beneficial effects of constant OM application for maintaining the nutrient status of soil[41].

    López-Fando & Pardo[42] quantified the impact of various tillage practices including NT, CT, minimum tillage (MT), and zone-tillage (ZT) on soil micronutrient stocks. Tillage systems did exhibit a significant influence on plant available Fe stocks in the topsoil; however, diminished with depth under ZT, NT and MT. Manganese was higher in NT and ZT at all depths and increased with soil depth. Zinc was highest under NT and other results did not vary significantly as in the case of Cu. The SOC levels were also found to be responsible to affect micronutrients due to tillage practices. Likewise, in Calciortidic Haploxeralf soil the distribution of soil micronutrients (Zn, Mn, Fe, Cu) was ascertained under different tillage practices (CT, MT, and NT). The micronutrient status was highest under NT in the upper layers due to the higher SOC level[43].

    Sharma & Dhaliwal[44] determined that the combined application of nitrogen and rice residues facilitated the transformation of micronutrients (Zn, Mn, Fe, Cu). Among different fractions, the predominant fractions were crystalline Fe bound in Zn, Mn, and Cu and amorphous Fe oxide in Fe with 120 kg N ha˗1 and 7.5-ton rice residue incorporation. The higher content of occluded fractions adduced the increment in cationic micronutrient availability in soil with residue incorporation together with N fertilization due to increased biomass. Rice straw compost along with sewage sludge (SS) and INF also affected the micronutrient availability under the RW cropping system. Nitrogen fertilization through inorganic fertilizers and rice straw compost and sewage sludge (50% + 50%) improved soil micronutrient status due to an increase in SOM over sole NPK fertilizers[45]. Earlier, Dhaliwal et al.[46] in a long-term experiment determined that different combinations of NPK along with biogas slurry as an organic source modified the extractable micronutrient status of the soil.

    A comparative study was carried out by Dhaliwal et al.[47] to ascertain the long-term impact of agro-forestry and rice–wheat systems on the distribution of soil micronutrients. The DTPA-extractable and total Cu, Zn, Fe, and Mn were greater in the RW system due to the reduced conditions because of rice cultivation. Under the RW system Zn removal was higher which was balanced by continuous Zn application. The higher availability of Fe under the RW system was due to reduced conditions. Contrarily, Mn was greater under the agro-forestry system owing to nutrient recycling from leaf litter.

    The long-term impact of integrated application of FYM, GM, WCS (wheat-cut straw) and INF on the soil micronutrients (Zn, Mn, Cu, and Fe) have been studied by Dhaliwal et al.[48]. The FYM application substantially improved DTPA-extractable Zn status followed by GM and WCS, whereas Cu content was maximum in the plots with OM application. The highest Fe concentration was recorded in treatment in which 50% recommended N supplied through FYM. This could be ascribed to the release of micronutrients from OM at low soil pH.

    Shiwakoti et al.[49] studied the dual effects of tillage methods (MP, DP, SW) and variable rates of N (0, 45, 90, 135 and 180 kg ha−1) on the distribution of micronutrients under a moisture-deficit winter wheat-fallow system. The soil Mn content was highest under the DP regime. Inorganic N application reduced Cu content in the soil. Comparative studies with adjacent undisturbed grass pasture indicated the loss of Zn and Cu to a significant extent. Thus, DP along with nitrogen added through inorganic fertilizers could improve micronutrient concentration in the soil. Moreover, the results implied that long-term cultivation with nitrogen fertilization and tillage results in the decline of essential plant nutrients in the soil. Thus, organic amendments along with INF may prove an effective approach to increase soil micronutrient content. In another study conducted by Lozano-García & Parras-Alcántara[50] tillage practices such as NT under apple orchard, CT with the wheat-soybean system and puddling (PD) in the rice-rice cropping system were found to affect nutrient status. Under CT, Cu content was lowest and Zn content was highest. On the contrary, puddling caused an increase in Fe and Mn concentration owing to the dispersion of soil aggregates which reduced the percolation of water and created an anaerobic environment thereby enhancing the availability of Fe and Mn.

    Tillage practices along with gypsum fertilization have been known to affect secondary nutrient concentrations in soil. In a long-term experiment, FYM application showed maximum response to increased S concentration due to the maximum addition of OM through FYM over other treatments as S is an essential component of OM and FYM[32]. Higher Mg content was recorded in FYM and pea vine treatments because the application of organic matter through organic manure or pea vines outright led to Mg accrual. The lower Mg concentration in topsoil than the lower layers was due to the competition between Mg and K for adsorbing sites and thus displacement of Mg by K. Han et al.[28] while ascertaining the impact of organic manures and mineral fertilizers (NPK) on soil chemical attributes determined that INF application reduced exchangeable calcium, whereas no significant changes were exhibited in the magnesium concentrations. The OM application significantly increased both the calcium and magnesium concentrations in the soil.

    While ascertaining the effect of different tillage treatments such as CT, NT, and MT on exchangeable and water-soluble cations, Lozano-García & Parras-Alcántara[50] recorded that NT had greater content of exchangeable Ca2+ and Mg2+ than MT and CT. The exchangeable Ca2+ decreased with depth, however, opposite results were observed for Mg2+ which might be due to the higher uptake of Mg2+ by the crop. On another note, there might be the existence of Mg2+-deficient minerals on the surface horizon. Alam et al.[51] studied the temporal effect of tillage systems on S distribution in the soil and observed that available S was 19%, 31%, and 34% higher in zero tillage than in minimum tillage, conventional tillage, and deep tillage, respectively.

    Kumar et al.[38] appraised the impact of tillage systems on surface soil nutrient dynamics under the following conditions: conventional tillage, zero till seeding with bullock drawn, conventional tillage with bullock drawn seeding, utera cropping and conservation tillage seeding with country plough and observed that tillage had a significant impact on the available S content. Compared with conventional tillage, zero and minimum tillage had higher S content as there was none or limited tillage operations which led to the accumulation of root stubble in the soil that decomposed over time and increased S concentration.

    Soil is considered a hotspot for microbial biodiversity which plays an important role in building a complex link between plants and soil. The microbial components exhibit dynamic nature and, therefore, are characterized as good indicators of soil quality[52]. These components include MBC, MBN, PMN and microbial respiration which not only assist in biological transformations like OM conversion, and biological nitrogen fixation but also increase nutrient availability for crop uptake. Management strategies such as fertilizer inputs and tillage practices may exert beneficial effects on soil biota as discussed below.

    Soil is an abode to a considerable portion of global biodiversity. This biodiversity not only plays a pivotal role in regulating soil functions but also provides a fertile ground for advancing global sustainability, especially agricultural ventures. Thus, the maintenance of soil biodiversity is of paramount importance for sustaining ecosystem services. Soil biodiversity is the diverse community of living creatures in the soil that interact not only with one another but also with plants and small animals to regulate various biological activities[53]. Additionally, it increases the fertility of soil by converting organic litter to SOM thereby enhancing SOC content. Thus, the SOM measures the number and activity of soil biota. Furthermore, the quality and amount of SOC, as well as plant diversity have a considerable impact on the soil microbial community structure[54].

    Dangi et al.[55] ascertained the impact of integrated nutrient management and biochar on soil microbial characteristics and observed that soil amended with biochar or the addition of organic manures influenced microbial community composition and biomass and crop yield. After two years, the higher rates of biochar significantly enhanced the levels of gram-positive and gram-negative bacterial phospholipid fatty acid (PLFA), total arbuscular mycorrhizal fungal (AMF) than lower rates, unfertilized and non-amended soil. Luan et al.[56] conducted a comparison study in a greenhouse to assess the effects of various rates of N fertilizer and kinds (inorganic and organic) on enzyme activities and soil microbial characteristics. Microbial growth (greater total PLFAs and microbial biomass carbon) and activity were promoted by manure substitution of mineral fertilizer, particularly at a higher replacement rate. On account of lower response in bacterial over fungal growth, manure addition led to a greater fungi/bacteria ratio. Furthermore, manure application significantly enhanced microbial communities, bacterial stress indicators and functional diversity. Lazcano et al.[57] determined the influence of different fertilization strategies on microbial community structure and function, soil biochemical properties and crop yield three months after addition of fertilizer. The integrated fertilizer regimes augmented microbial growth with improved enzyme activity as compared to sole inorganic amendments. Bacterial growth showed variable response with variation in fertilizer regime used whereas fungal growth varied with the amount of fertilizer added. Compared to mineral fertilizers, manure application led to a rapid increase in PLFA biomarkers for gram-negative bacteria. The organic amendments exhibited significant effects even at small concentration of the total quantity of nutrients applied through them; thus, confirming the viability of integrated fertilizer strategies in the short term.

    Kamaa et al.[58] assessed the long-term effect of crop manure and INF on the composition of microbial communities. The organic treatments comprised of maize (Zea mays) stover (MS) at 10 t ha−1 and FYM @ 10 t ha−1, INF treatments (120 kg N, 52.8 kg P-N2P2), integrated treatments (N2P2 + MS, N2P2 + FYM), fallow plot and control. The treatment N2P2 exhibited unfavourable effects on bacterial community structure and diversity that were more closely connected to the bacterial structure in control soils than integrated treatments or sole INF. In N2P2, fungal diversity varied differently than bacterial diversity but fungal diversity was similar in the N2P2 + FYM and N2P2 + MS-treated plots. Thus, the total diversity of fungal and bacterial communities was linked to agroecosystem management approaches which could explain some of the yield variations observed between the treatments. Furthermore, a long-term experiment was performed by Liu et al.[59] to study the efficiency of pig manure and compost as a source for N fertilization and found unique prokaryotic communities with variable abundance of Proteobacteria under compost and pig manure treatments.

    Recently, Li et al.[60] assessed the influence of different tillage practices (no-tillage, shallow tillage, deep tillage, no-tillage with straw retention, shallow tillage with straw retention and deep tillage with straw retention) on microbial communities and observed that tillage practices improved the bacterial Shannon index to a greater extent over the no-tillage plots in which the least value was recorded. Another research study by He et al.[61] reported the effect of tillage practices on enzyme activities at various growth stages. Across all the growth stages, enzyme activities of cellobiohydrolase (CBH), β-xylosidase (BXYL), alkaline phosphatase (AP), β-glucosidase (BG), β-N-acetylglucosamines (NAG) were 17%−169%, 7%−97%, 0.12%−29%, 3%−66%, 23%−137% greater after NT/ST, NT, ST, ST/PT, and PT/NT treatments as compared to plow tillage. The NT/ST treatment resulted in highest soil enzyme activities and yield, and thus was an effective and sustainable method to enhance soil quality and crop production.

    Microbes play a crucial role in controlling different soil functions and soil ecology and microbial community show significant variation across as well as within the landscape. On average, the total biomass of microbes exceeds 500 mg C kg soil−1[62]. Microbial biomass carbon is an active constituent of SOM which constitutes a fundamental soil quality parameter because SOM serves as a source of energy for microbial processes and is a measure of potential microbial activity[48,63]. Soil systems that have higher amounts of OM indicate higher levels of MBC. Microbial biomass carbon is influenced by many parameters like OM content in the soil, land use, and management strategies[64]. The MBC and soil aggregate stability are strongly related because MBC integrates soil physical and chemical properties responds to anthropogenic activities.

    Microbial biomass is regarded as a determinative criterion to assess the functional state of soil. Soils having high functional diversity of microbes which, by and large, occurs under organic agricultural practices, acquire disease and insect-suppressive characteristics that could assist in inducing resistance in plants[65]. Dou et al.[66] determined that soil microbial biomass C (SMBC) was 5% to 8% under wheat-based cropping systems and zero tillage significantly enhanced SMBC in the 0−30 cm depth, particularly in the upper 0 to 5 cm. According to Liang et al.[67], SMBC and soil microbial biomass N (SMBN) in the 0−10 cm surface layer were greater in the fertilized plots in comparison to the unfertilized plots on all sampling dates whereas microbial biomass C and N were highest at the grain filling stage. Mandal et al.[68] demonstrated that MBC also varied significantly with soil depth. Surface soil possessed a maximum MBC value than lower soil layers due to addition of crop residues and root biomass on the surface soil. The MBC content was highest with combined application of INF along with farmyard manure and GM, whereas untreated plots showed minimum MBC values. The incorporation of CR slows down the rate of mineralization processes; therefore, microbes require more time to decompose the residues and utilize the nutrients released[69]. On the other hand, incorporation of GR having a narrow C:N ratio enhances microbial activity and consequently accelerates mineralization in the soil. Malviya[70] also recorded that the SMBC contents were significantly greater under RT than CT, regardless of soil depth which was also assigned to residue incorporation which increases microbial biomass on account of higher carbon substrate in RT.

    Naresh et al.[71] studied the vertical distribution of MBC under no-tillage (NT), shallow (reduced) tillage and normal cultivated fields. A shallow tillage system significantly altered the tillage induced distribution of MBC. In a field experiment, Nakhro & Dkhar[72] examined the microbial populations and MBC in paddy fields under organic and inorganic farming approaches. The organic source used was a combination of rock phosphate, FYM and neem cake, whereas a mixture of urea, muriate of potash and single super phosphate was used as an inorganic source. The organically treated plots exhibited the highest MBC compared to inorganically treated plots and control. Organic carbon exhibited a direct and significant correlation with bacterial and fungal populations. The addition of organic fertilizers enhanced the content of SOC and consequently resulted in higher microbial count and MBC. Ramdas et al.[73] investigated the influence of inorganic and organic sources of nutrients (as minerals or INF) applied over a five-year period on SOC, MBC and other variables. It was observed that the addition of FYM and conjoint application of paddy straw (dry) and water hyacinth (PsWh) (fresh) significantly increased the SOC content than vermicompost, Chromolaena adenophorum (fresh) and Glyricidia aculeate (fresh), and Sesbania rostrata (fresh).

    Xu et al.[74] evaluated the influence of long-term fertilization strategies on the SOC content, soil MBN, soil MBC, and soil microbial quotient (SMQ) in a continuous rice system and observed that MBC at the main growth stages of early and late rice under 30% organic matter and 70% mineral fertilizer and 60% organic matter and 40% mineral fertilizer treatments was greater as compared to mineral fertilizer alone (MF), rice straw residues and mineral fertilizer (RF), and no fertilizer (CK) treatments. However, SMBC levels at late growth stages were greater in comparison to early growth stages. A recent study by Xiao et al.[75] demonstrated that increasing tillage frequency (no-tillage, semi-annual tillage, and tillage after every four months, two months, and one month) decreased soil MBC. Microbial biomass carbon content was significantly greater in no-till treatment (597 g kg−1) than in tillage every four months (421 g kg−1), two months (342 g kg−1) and one month (222 g kg−1). The decrease in the content of MBC in association with tillage practices is due to soil perturbations which enhanced soil temperature, diminished soil moisture content, and resulted in the destruction of microbial habitat and fungal hyphae. Therefore, the MBC content eventually affected the N cycle.

    Li et al.[76] reported that in comparison to CT, NT and RT resulted in increased MBC content and NT significantly increased MBC by 33.1% over CT. Furthermore, MBC concentration was 34.1% greater in NT than RT. The increase in MBC concentration was correlated with the results of increase in SOC concentration. Site-specific factors including soil depth and mean annual temperature significantly affected the response ratio of MBC under NT as compared to the duration of NT.

    Microbial biomass nitrogen (MBN) is a prominent indicator of soil fertility as it quantifies the biological status of soil. Soil MBN is strongly associated with organic matter of the soil. The nitrogen in MBN has a rapid turnover rate thereby reflecting the changes in management strategies way before the transformations in total N are discernable[77].

    In an experiment on continuous silage maize cultivation with crop rotation, Cerny et al.[78] observed that organic fertilizers exerted an affirmative influence on the soil MBN. During the application of organic manure MBN decreased, but there was higher MBN content as compared to control. However, addition of mineral nitrogenous fertilizers exerted an adverse effect on MBN content in experiments with maize. El-Sharkawi[79] recorded that organic matter-treated pots resulted in maximum MBN content than urea-treated pots. The sludge application enhanced total MBN and, therefore, could implicitly benefit crop production particularly in poor soils[18]. Sugihara et al.[80] observed that during the grain-filling stage in maize, residue and/or fertilizer addition exerted a pronounced influence on soil microbial dynamics; however, a clear effect of residue and ⁄or fertilizer addition was not observed. Microbial biomass nitrogen reduced dramatically from 63–71 to 18–33 kg N ha˗1 and C:N ratio at the same time increased more than ten-fold in all plots.

    Malik et al.[81] apprised that the organic amendments significantly enhanced MBN concentrations up to 50% more than the unamended soil. Wang et al.[82] evaluated the influence of organic materials on MBN content in an incubation and pot experiment with acidic and calcareous soils. The results revealed that MBN content which was affected by the different forms of organic amendments, increased by 23.37%−150.08% and 35.02%−160.02% in acidic and calcareous soils, respectively. The MBN content of both soils decreased with the increase in the C/N ratio of the organic materials, though a higher C/N ratio was effective for sustaining a greater MBN content for a very long time.

    Dhaliwal & Bijay-Singh[52] observed higher MBN levels in NT soils (116 kg ha−1) than in cultivated soils (80 kg ha−1). Kumar et al.[83] ascertained that in surface layer, MBN content was 11.8 mg kg−1 in CT which increased to 14.1 and 14.4 mg kg−1 in ZT and RT without residue retention and 20.2, 19.1 and 18.2 mg kg−1 in ZT, RT and CT with residue incorporation, respectively (Table 1). In the subsurface layer, the increased tendency on account of tillage and crop residue retention was identical to those of 0−15 cm layer but the magnitude was comparatively meagre (Table 1). In comparison to control, the persistent retention of crop residues led to significant accrual of MBN in the surface layer.

    Table 1.  Effect of different treatments on contents of various fractions of soil organic carbon[38].
    TreatmentsPMN (mg kg−1)MBC (mg kg−1)MBN (mg kg−1)DOC (mg kg−1)
    Depths (cm)
    0−1515−300−1515−300−1515−300−1515−30
    Tillage practices
    ZTR12.411.2562.5471.120.218.9198.6183.6
    ZTWR8.57.6350.4302.114.112.6167.1159.2
    RTR10.69.9490.2399.319.117.2186.4171.6
    RTWR7.66.6318.1299.814.413.7159.5148.7
    CTR9.38.5402.9354.418.216.6175.9168.9
    CT6.75.6307.9289.511.89.7142.5134.6
    Nitrogen management
    Control3.62.8218.3202.910.810.4103.792.3
    80 kg N ha−15.34.4241.1199.414.912.2128.3116.9
    120 kg N ha−18.97.6282.7220.916.516.1136.8123.6
    160 kg N ha−19.88.4343.9262.919.418.1164.8148.9
    200 kg N ha−110.49.7346.3269.622.721.7155.7136.4
    ZTR = Zero tillage with residue retention, ZTWR = Zero tillage without residue retention; RTR = Reduced tillage with residue retention, RTWR = Reduced tillage without residue retention, CTR = Conventional tillage with residue incorporation; CT = Conventional tillage without residue incorporation.
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    Xiao et al.[75] determined that the MBN content decreased with tillage treatment having highest value in no tillage treatment, however, the difference among the treatments was negligible. Soil perturbations decreased the aggregate size and thus lower the soil aeration and exposure of fresh organic matter which restricted the growth of microorganisms. The results also concluded that MBN content is highly sensitive to tillage. Ginakes et al.[84] assessed the impact of zone tillage intensity on MBN in a corn-kura clover cropping sequence. Microbial biomass nitrogen was influenced by time and type of tillage treatment. Temporal studies revealed that MBN was higher after tillage treatment than the values possessed before tillage. Under different tillage treatments, higher values were recorded in ST (shank-till) and DT (double-till) over NT and RZT (zone-till) treatments.

    Another biological parameter, PMN, is a crucial parameter of soil fertility due to its association with soil N supply for crop growth. Also, PMN indicates the status of soil microbial community associated with PMN, whether it is improving or degrading. Forest soils are characterized by greater levels of PMN than CT receiving conventional chemical fertilizers which could be assignable to improved microbial activity in the former soils than the latter[48,77]. Aulakh et al.[85] assessed the effect of various combinations of fertilizer N, P, FYM and wheat residue (WR) applied to soybean and soybean residues added to wheat under CT and CA. The added fertilizers of N and P, FYM, and crop residue enhanced the mean weight diameter and water-stable aggregates thus favoured the development of macro-aggregates. The treatment INF + FYM + crop residue performed better among all the treatments. The net flux of mineral nitrogen from the mineralizable fraction is used to measure potentially mineralizable N which indicates the balance between mineralization and immobilization by soil microbes[77]. Nitrogen mineralization is widely used to assess the ability of SOM to supply inorganic nitrogen in the form of nitrate which is the most common form of plant-available nitrogen. Kumar et al.[83] observed an increase in PMN which was higher in surface soil than sub-surface soil thereby implying that high OC accumulation on account of crop residue retention was the most probable cause.

    Verma & Goyal[86] assessed the effect of INM and organic manuring on PMN and observed that PMN was substantially affected by different organic amendments. Potentially mineralizable nitrogen varied between 19.6−41.5 mg kg−1 soil with greater quantity (2.5%) in vermicompost applied plots than FYM treated plots. The INF treatments resulted in lower PMN content which could be due to nutrient immobilization by microbes. Mahal et al.[87] reported that no-till resulted in higher PMN content than conventional tillage treatments. This trend was due to the maintenance of SOM due to the residue cover and reduction of soil erosion under no-tillage system[88]. On the contrary, tillage practices led to the loss of SOC owing to loosened surface soil and higher mineralization of SOM.

    Soil respiration is referred as the sum of CO2 evolution from intact soils because of the respiration by soil organisms, mycorrhizae and roots[89]. Various researchers have proposed soil respiration as a potential indicator of soil microbial activity[52,77]. Gilani & Bahmanyar[90] observed that addition of organic amendments enhanced soil respiration more than the control and synthetic fertilizer treatments. Moreover, among organic amendment treatments, highest soil respiration was observed in sewage-sludge treated soils. Under controlled conditions in saline-sodic soil, Celis et al.[91] reported that sewage sludge resulted in a higher soil respiration rate than mined gypsum and synthetic gypsum. The application of gypsum because of minimal organic matter intake had little effect on soil respiration. The addition of organic matter especially during early spring led to higher microbial biomass and soil respiration albeit diminished levels of nitrate-N. Moreover, SOM hinders the leaching of nitrate ions thereby resulting in a better soil chemical environment[71].

    Faust et al.[92] observed that microbial respiration was associated with volumetric water content. The respiration declined with less availability of water, thus the lesser the tillage intensity, the more the volumetric water content which consequently resulted in higher microbial respiration. Another study by Bongiorno et al.[93] reflected the influence of soil management intensity on soil respiration. Reduced tillage practices resulted in 51% higher basal respiration than CT. Furthermore, this investigation suggested that microbial catabolic profile could be used as a useful biological soil quality indicator. Recently, Kalkhajeh et al.[94] ascertained the impact of simultaneous addition of N fertilizer and straw-decomposing microbial inoculant (SDMI) on soil respiration. The SDMI application boosted the soil microbial respiration which accelerated the decomposition of straw due to N fertilization. The C/N ratio did not affect the microbial respiration at elongation and heading stages, whereas N fertilization enhanced the microbial respiration to a greater extent than the unfertilized control. Additionally, the interaction between sampling time and basal N application significantly affected microbial respiration.

    Gong et al.[95] apprised the effect of conventional rotary tillage and deep ploughing on soil respiration in winter wheat and observed that deep ploughing resulted in a higher soil respiration rate than conventional rotary tillage. Soil moisture content and temperature are the dominating agents influencing soil respiration which is restricted by the soil porosity.

    Soil organic carbon plays a vital role in regulating various soil functions and ecosystem services. It is influenced by numerous factors like tillage practices and fertilization. Moreover, modified management practices may prove beneficial to avoid SOC loss by increasing its content. An exogenous supply of fertilizers may alter the chemical conditions of soil and thus result in transformation of SOC. Tillage practices lead to frequent soil disturbances which reduce the size of soil aggregates and accelerate the oxidation of SOC thereby reducing its content. The literature on the influence of fertilization and tillage practices on the transformation of SOC is discussed below.

    Soil organic carbon is a major part of the global carbon cycle which is associated not only with the soil but also takes part in the C cycling through vegetation, oceans and the atmosphere (Figs 1 & 2). Soil acts as a sink of approximately 1,500 Pg of C up to 1 m depth, which is greater than its storage in the atmosphere (approximately 800 Pg C) and terrestrial vegetation (500 Pg C) combined[96]. This dynamic carbon reservoir is continuously cycling in diverse molecular forms between the different carbon pools[97]. Fertilization (both organic and mineral) is one of the crucial factors that impart a notable influence on OC accretion in the soil. Many researchers have studied the soil C dynamics under different fertilizer treatments. Though inorganic fertilizers possess the advantage of easy handling, application and storage, they do not contribute to soil organic carbon. On the contrary, regardless of management method, plant residues are known to increase organic carbon content.

    Figure 1.  Impact of different fertilization regimes on abundance of the microbial biomarker groups . Error bars represent the standard error of the means and different letters indicate significant differences at p < 0.05 among treatments. Source: Li et al.[60].
    Figure 2.  Soil organic carbon (SOC) dynamics in the global carbon cycle.

    Katkar et al.[98] reported a higher soil quality index under conjunctive nutrient management strategies comprising addition of compost and green leaves along with mineral nutrients. Mazumdar et al.[99] investigated the impact of crop residue (CR), FYM, and leguminous green manure (GM) on SOC in continuous rice-wheat cropping sequence over a 25-year period. At the surface layer, the maximum SOC content was recorded under NPK + FYM than NPK + CR and NPK + GM treatments. SOC was significantly lower under sole application of INFs (NPK) than the mixed application of organic and inorganic treatments. A higher range of SOC content was recorded at a depth of 0.6 m in the rice-wheat system (1.8–6.2 g kg−1) in farmyard manure (FYM)-treated plots than 1.7–5.3 g kg−1 under NPK, and 0.9–3.0 g kg−1 in case of unfertilized plots[100]. In a research study Dutta et al.[101] reported that rice residue had a higher decomposition rate (k¼ 0.121 and 0.076 day−1) followed by wheat (0.073 and 0.042 day−1) and maize residues (0.041 day−1) when their respective residues placed on soil surface than incorporated in the soils. Naresh et al.[102] found FYM and dhaincha as GM/ sulphitation press mud (SPM) treatments are potent enough to enhance the SOC. Maximum SOC content was noted in 0–5 cm depth that reduced gradually along the profile. In surface soil, the total organic content (TOC) under different treatments varied with source used to supply a recommended dose of nitrogen (RDN) along with conventional fertilizer (CF).

    Cai et al.[103] ascertained that long-term manure application significantly improved SOC content in different size fractions which followed the sequence: 2,000–250 μm > 250–53 μm > 53 μm fraction. Naresh et al.[22] determined that mean SOC content increased from 0.54% in control to 0.65% in RDF and 0.82% in RDF + FYM treatment and improved enzyme activity; thus, ultimately influenced nutrient dynamics under field conditions. The treatments RDF + FYM and NPK resulted in 0.28 Mg C ha−1 yr−1 and 0.13 Mg C ha−1 yr−1, respectively and thus higher sequestration than control. Zhao et al.[104] determined that in the surface layer, significant increase in SOC content in each soil aggregate was noticed under straw incorporation treatments over no straw incorporated treatments (Fig. 3). Moreover, the aggregate associated OC was significantly higher in the surface layer than the sub-surface layer. The highest increment in aggregate-associated OC was noted in both maize and wheat straw (MR-WR) added plots followed by MR and least in WR. Besides, all of the three straw-incorporated treatments exhibited notable increase in SOC stock in each aggregate fraction in the surface layer of the soil. In the subsurface (20−40 cm) layer under MR-WR, significant rise in SOC stock of small macro-aggregates was observed, whereas there was a reduction in SOC stock in the silt + clay fraction than other treatments. The straw-incorporated treatments increased the quantity of mineral-associated organic matter (mSOM) and intra-aggregate particulate organic matter, (iPOM) within small macro-aggregates and micro-aggregates especially in the topmost layer of the soil.

    Figure 3.  Distribution of OC in coarse iPOM (intra-aggregate particulate organic matter) fine iPOM, mSOM (mineral-associated matter), and free LF (free light fraction) of small macro-aggregates and micro-aggregates in the 0–20 cm and 20–40 cm soil layers under MR-WR (return of both maize and wheat straw), MR (maize straw return), WR (wheat straw return). Different lowercase and uppercase letters indicate significant differences at p < 0.05 among treatments and depths respectively[104].

    Srinivasarao et al.[105] reported that SOC content was reduced with the addition of INFs (100% RDN) alone as compared to the conjunctive application of inorganic and organic or sole FYM treatments. Earlier, Srinivasarao et al.[106] reported that FYM treated plots exhibited greater per cent increase in SOC stock than mineral fertilized plots and control. Tong et al.[107] ascertained that the application of NP and NPK significantly improved SOC stocks. On the contrary, fertilized soils could also exhibit decrease in carbon content than control. Naresh et al.[108] determined that higher biomass C input significantly resulted in greater particulate organic carbon (POC) content. Zhang et al.[109] ascertained that long-term addition of NPK and animal manures significantly improved SOC stocks by a magnitude of 32%−87% whereas NPK and wheat/ and or maize straw incorporation enhanced the C stocks by 26%−38% than control. Kamp et al.[110] determined that continuous cultivation without fertilization decreased SOC content by 14% than uncultivated soil. However, super optimum dose of NPK, balanced NPK fertilization and integration of NPK with FYM not only improved SOC content but also SOC stocks over the first year. In conventionally tilled cotton-growing soils of southern USA, Franzluebbers et al.[111] estimated that carbon sequestration averaged 0.31 ± 0.19 Mg C ha−1 yr−1. Mandal et al.[112] reported maximum SOC stock in the surface layer of the soil (0–15 cm) which progressively diminished with depth in each land use system. A significant decrease in SOC stock along the profile depth was also observed by Dhaliwal et al.[47] in both croplands and agroforestry. In the topmost soil layer, highest SOC stock was recorded in rice–fallow system while the lowest was in the guava orchard[112].

    Nath et al.[113] determined that there was accrual of higher TOC in surface layers as compared to lower layers of soil under paddy cultivation. This accrual could be adduced to left-over crop residues and remnant root biomass which exhibited a decreasing trend with soil depth. Das et al.[114] determined that integrated use of fertilizers and organic sources resulted in greater TOC as compared to control or sole fertilizer application. Fang et al.[115] observed that the cumulative carbon mineralization differed with aggregate size in top soils of broad-leaved forests (BF) and coniferous forests (CF). However, in deep soil it was greater in macro-aggregates as compared to micro-aggregates in BF but not in CF (Fig. 4). By and large, the percent SOC mineralized was greater in macro-aggregates as compared to micro-aggregates. Dhaliwal et al.[100] ascertained that SOC accrual was considerably influenced by residue levels and tillage in surface soil (0−20 cm); albeit no variation was observed at lower depth (20−40 cm). The SOC content was greater in zero-tilled and permanently raised beds incorporated with residues as compared to puddled transplanted rice and conventionally planted wheat. Pandey et al.[116] reported that no-tillage prior to sowing of rice and wheat increased soil organic carbon by 0.6 Mg C ha–1 yr–1. The carbon sequestration rate on account of no-tillage or reduced tillage ranged between 0−2,114 kg ha–1 yr–1 in the predominant cropping system of South Asia, Xue et al.[117] observed that the long-term conventional tillage, by and large, exhibited a significant decline in SOC owing to degradation of soil structure, exposing protected soil organic matter (intra-soil aggregates) to microbes. Therefore, the adoption of no-tillage could hamper the loss of SOC thereby resulting in a greater or equivalent quantity of carbon in comparison to CT (Fig. 5).

    Figure 4.  (a) Soil aggregate fractions of two depths in two restored plantations of subtropical China, (b) organic carbon and (c) its mineralization from various soil aggregates within 71 d at various soil depths in two restored plantations of subtropical China. Error bars show the standard error of the mean. The different letters represent significant differences among the different soil aggregate fractions within a depth at p < 0.05[115].
    Figure 5.  The concentrations of (a) SOC, (b) total nitrogen (TN), and (c) soil C:N ratio for 0–50 cm profile under different tillage treatments in 2012 and 2013. NT = no-till with residue retention; RT = rotary tillage with residue incorporation; PT = plow tillage with residue incorporation; and PT0 = plow tillage with residue removed. The lowercase letters indicate statistical difference among treatments at p < 0.05[117].

    Singh et al.[118] determined that carbon stock in the 0-40 cm layer increased by 39, 35 and 19% in zero-tilled clay loam, loam, and sandy loam soils, respectively as compared to conventional tilled soils over a period of 15 years. Kuhn et al.[119] also apprised about the advantages of NT over CT vis-a-vis SOC stocks across soil depths. In the surface layer (0−20 cm) NT, by and large, resulted in higher SOC stocks as compared to CT; however, SOC stocks exhibited a declining trend with soil depth, in fact, became negative at depths lower than 20 cm. Sapkota et al.[120] observed that over a period of seven years, direct dry-seeded rice proceeded by wheat cultivation with residue retention enhanced SOC at 0-60 cm depth by a magnitude of 4.7 and 3.0 t C ha−1 in zero-tillage (ZTDSR-ZTW + R) and without tillage (PBDSR-PBW + R), respectively. On the contrary, the conventional tillage rice-wheat cropping system (CTR-CTW) decreased the SOC up to 0.9 t C ha−1 (Table 2).

    Table 2.  Influence of tillage and crop establishment methods on SOC stock and its temporal variation under rice–wheat system[120].
    Tillage and crop establishment methodsDepths (m)
    0–0.050.05–0.150.15–0.30.3–0.60–0.6
    Total SOC (t/ha)
    CTR-CTW3.5e7.1c8.77.026.2c
    CTR-ZTW3.9d7.6bc8.86.526.7c
    ZTDSR-CTW4.2d7.5bc9.26.327.3c
    ZTDSR-ZTW4.9c8.9ab8.26.228.2bc
    ZTDSR-ZTW+R6.1a9.0ab9.86.831.8a
    PBDSR-PBW+R5.5b9.3a9.36.030.1ab
    MSD0.41.72.01.42.49
    Treatment effect
    (p value)
    < 0.0010.040.1580.267< 0.001
    Initial SOC content3.6 ±
    0.15
    8.1 ±
    1.39
    8.78 ±
    1.07
    6.7 ±
    0.73
    27.1 ±
    1.21
    Change in SOC over seven years (t/ha)
    CTR-CTW−0.16−0.99−0.040.28−0.90
    CTR-ZTW0.28−0.500.01−0.20−0.41
    ZTDSR-CTW0.62−0.570.45−0.340.16
    ZTDSR-ZTW1.340.84−0.62−0.461.09
    ZTDSR-ZTW+R2.490.961.040.164.66
    PBDSR-PBW+R1.891.220.51−0.642.98
    CTR-CTW = Conventionally tilled puddled transplanted rice followed by conventionally tilled wheat, CTR-ZTW = Conventionally tilled puddled transplanted rice followed by zero-tilled wheat, ZTDSR-CTW = Zero-tilled direct dry-seeded rice followed by conventionally tilled wheat, ZTDSR-ZTW = Zero-tilled direct dry-seeded rice followed by zero-tilled wheat, ZTDSR-ZTW+R = Zero-tilled direct dry-seeded rice followed by zero-tilled wheat with residue retention, PBDSR-PBW+R = Direct dry-seeded rice followed by direct drilling of wheat both on permanent beds with residue retention, MSD, minimum significant difference. Significant different letters indicate significant differences at p < 0.05.
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    Labile organic carbon (LC) is that fraction of SOC that is rapidly degraded by soil microbes, therefore, having the highest turnover rate. This fraction can turn over quickly on account of the change in land use and management strategies. From the crop production perspective, this fraction is crucial as it sustains the soil food cycle and, hence, considerably impacts nutrient cycling thereby altering soil quality and productivity. Short-term management could influence the labile fraction of carbon[121]. However, some site-specific problems and regional factors may influence their distribution in soil layers[102].

    Banger et al.[122] observed significant alteration in labile pools of C, for instance, particulate organic matter (POM), water-soluble C (WSC) and light fraction of C (LFC) because of the addition of fertilizers and/or FYM over a 16-year period. Particulate organic matter, LFC and WSC contributed 24%–35%, 12%–14% and 0.6%–0.8%, respectively, towards SOC. The increase in concentration of SOC including its pools like POC and the sequestration rate due to integrated nutrient management was also reported by Nayak et al.[123]. Gu et al.[124] observed that mulch-treated soils (straw and grass mulch) had significantly greater levels of LOC, POC, DOC and EOC as compared to no mulch-treated soils which could be adduced to the addition of straw, root and its sections into the soil. The content of labile C fractions across all treatments exhibited a decreasing trend with soil depth[23, 102, 125].

    In a long-term experiment, Anantha et al.[126] observed that the total organic carbon apportioned into labile carbon, non-labile, less labile, and very labile carbon constituted around 18.7%, 19.3%, 20.6% and 41.4% of the TOC, respectively (Table 3). Zhu et al.[20] determined that straw incorporation had a substantial impact on TOC and labile C fractions of the soil which were greater in straw incorporated treatments as compared to non-straw treatments across all the depths. Wang et al.[127] reported that the light fraction organic carbon (LFOC) and DOC were significantly greater in the straw-applied treatments than the control by a magnitude of 7%–129% for both the early and late season rice. The treatments NPK + FYM or NPK + GR + FYM resulted in greater content of very labile and labile C fractions whereas non-labile and less labile fractions were greater in control and NPK + CR treatment. There was 40.5% and 16.2% higher C build-up in sole FYM treated plots and 100% NPK + FYM, respectively over control. On the other hand, a net depletion of 1.2 and 1.8 Mg ha−1 in carbon stock was recorded under 50% NPK and control treatments, respectively. Out of the total C added through FYM, only 28.9% was stabilized as SOC, though an external supply of OM is a significant source of soil organic carbon[69]. Hence, to sustain the optimum SOC level at least an input of 2.3 Mg C ha−1 y−1 is required. A comparatively greater quantity of soil C in passive pools was observed in 100% NPK + FYM treatment. The increase in allocation of C into the passive pool was about 33%, 35%, 41% and 39% of TOC in control, suboptimal dose, optimal dose and super optimal dose of NPK which indicates that the concentration of passive pools increased with an increase in fertilization doses. Water-soluble carbon (WSC) was 5.48% greater in the upper soil layer as compared to lower layer of soil. In surface soil (0−15 cm), the values of light fraction carbon (LFC) were 81.3, 107.8, 155.2, 95.7, 128.8, 177.8 and 52.7 mg kg−1 in ZT without residue retention, ZT with 4 t ha−1 residue retention, ZT with 6 t ha−1 residue retention, FIRB without residue addition and FIRB with 4 and 6 t ha−1 residue addition and CT, respectively (Table 4). Tiwari et al.[128] determined that the decrease in POC was due to reduction in fine particulate organic matter in topsoil whereas decrement in dissolved organic carbon was observed largely in subsoil. Therefore, in surface soils fine POC and LFOC might be regarded as preliminary evidence of organic C alteration more precisely, while DOC could be considered as a useful indicator for subsoil. Reduction in allocations of fine POC, LFOC and DOC to SOC caused by tillage and straw management strategies indicated the decline in quality of SOC. A higher SOC concentration was recorded in the conjoint application of INF + FYM (0.82%) and sole application of INF (0.65%) than control (0.54%). Kumar et al.[83] reported that the CT without residue retention had significantly lower labile carbon fractions (27%–48%) than zero-tillage with 6-ton residue retention. Moreover, residue-retained fertilized treatments had significantly greater labile fractions of C than sole fertilized treatments[125]. Kumar et al.[83] reported highest change in DOC in zero-till with residue retention (28.2%) in comparison to conventional tillage practices. In ZT, absence of soil perturbations resulted in sustained supply of organic substrata for soil microbes which increases their activity. On the contrary, CT practices resulted in higher losses of C as CO2 due to frequent disturbances.

    Table 3.  Oxidisable organic carbon fractions in soils (g kg−1) at different layers[126].
    TreatmentDepths (cm)
    0−1515−3030−45Total
    Very Labile C
    Control3.6 ± 0.5c1.4 ± 0.3b1.3 ± 0.2a6.3 ± 0.4b
    50% NPK4.6 ± 0.3bc2.1 ± 0.7ab1.5 ± 0.1a8.1 ± 0.9a
    100% NPK4.4 ± 0.3bc2.3 ± 0.2a1.4 ± 0.5a8.0 ± 0.7a
    150% NPK5.0 ± 0.2ab2.6 ± 0.2a1.5 ± 0.1a9.0 ± 0.3a
    100% NPK + FYM4.8 ± 0.2ab2.0 ± 0.2ab1.3 ± 0.3a8.1 ± 0.2a
    FYM5.9 ± 1.3a2.2 ± 0.2a1.4 ± 0.3a9.5 ± 1.6a
    Fallow4.2 ± 0.7bc1.5 ± 0.5b0.7 ± 0.3b6.3 ± 0.8b
    Lbile C
    Control2.4 ± 0.3a1.0 ± 0.2a0.8 ± 0.4a4.2 ± 0.6a
    50% NPK1.7 ± 0.4ab0.9 ± 0.5a0.7 ± 0.2a3.3 ± 0.7a
    100% NPK1.8 ± 0.4ab0.8 ± 0.5a0.6 ± 0.3a3.2 ± 0.8a
    150% NPK1.2 ± 0.3b0.7 ± 0.2a0.9 ± 0.2a2.8 ± 0.4a
    100% NPK + FYM1.9 ± 0.3ab0.7 ± 0.2a0.7 ± 0.3a3.4 ± 0.2a
    FYM2.5 ± 0.9a0.7 ± 0.3a0.7 ± 0.2a3.9 ± 0.9a
    Fallow2.2 ± 1.0ab1.0 ± 0.3a1.0 ± 0.4a4.1 ± 1.1a
    Less labile C
    Control1.5 ± 0.3c0.6 ± 0.4c0.4 ± 0.0c2.6 ± 0.7d
    50% NPK1.8 ± 0.1c0.4 ± 0.1c0.5 ± 0.2c2.7 ± 0.1cd
    100% NPK2.5 ± 0.3ab0.8 ± 0.1bc1.1 ± 0.2ab4.4 ± 0.1b
    150% NPK2.6 ± 0.2a0.9 ± 0.1bc0.4 ± 0.2c3.9 ± 0.1b
    100% NPK + FYM2.7 ± 0.6a1.5 ± 0.2a1.4 ± 0.1a5.6 ± 0.7a
    FYM1.9 ± 0.7bc1.7 ± 0.2a1.0 ± 0.2b4.5 ± 0.7ab
    Fallow1.5 ± 0.3c1.3 ± 0.7ab0.9 ± 0.4b3.8 ± 1.2bc
    Non labile C
    Control1.2 ± 0.5b1.2 ± 0.3a0.2 ± 0.2b2.6 ± 0.5b
    50% NPK1.2 ± 0.9b1.7 ± 0.8a0.7 ± 0.4ab3.5 ± 1.8ab
    100% NPK1.3 ± 0.6b1.5 ± 0.6a0.5 ± 0.2ab3.3 ± 1.0ab
    150% NPK1.4 ± 0.3b1.5 ± 0.2a0.8 ± 0.1a3.7 ± 0.3ab
    100% NPK + FYM2.0 ± 0.8b1.3 ± 0.1a0.3 ± 0.3ab3.5 ± 0.7ab
    FYM3.7 ± 1.3a1.0 ± 0.2a0.5 ± 0.5ab5.1 ± 1.9a
    Fallow2.1 ± 0.2b1.4 ± 0.7a0.4 ± 0.2ab3.9 ± 0.9ab
    Values in the same column followed by different letters are significantly different at p < 0.001, ± indicates the standard deviation values of the means.
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    Table 4.  Influence of tillage and nitrogen management on distribution of carbon fractions in soil[83].
    TreatmentsWSOC
    (g kg−1)
    SOC
    (g kg−1)
    OC
    (g kg−1)
    BC
    (g kg−1)
    POC
    (mg kg)
    PON
    (mg kg−1)
    LFOC
    (mg kg−1)
    LFON
    (mg kg−1)
    Depths (cm)
    0−1515−300−1515−300−1515−300−1515−300−1515−300−1515−300−1515−300−1515−30
    Tillage practices
    ZTR28.826.223.119.39.619.134.694.281342.8967.9119.5108.1194.7154.814.812.3
    ZTWR25.324.618.414.87.877.213.763.19981.1667.494.686.5120.5104.711.810.3
    RTR27.025.922.418.28.688.174.133.871230.2836.9109.797.8170.9144.913.711.6
    RTWR23.721.818.114.27.667.073.122.96869.4604.482.676.6107.197.39.78.6
    CTR26.124.421.817.48.497.963.823.481099.1779.498.489.3143.8115.912.810.9
    CT21.820.916.113.16.215.642.892.63617.5481.869.257.690.873.69.67.9
    Nitrogen management
    Control21.114.916.113.16.135.481.581.07709.7658.631.726.3123.9104.36.45.8
    80 kg N ha−128.321.217.814.76.466.162.461.75860.7785.668.456.2132.8116.17.66.9
    120 kg N ha−129.522.119.116.17.256.713.262.18952.2808.989.578.5150.6127.69.78.6
    160 kg N ha−130.223.120.818.27.757.283.822.661099.5823.896.883.4168.5145.710.29.8
    200 kg N ha−131.125.421.318.77.937.484.153.421153.1898.4103.997.3176.2152.911.710.6
    WSOC = Water soluble organic carbon, SOC = Total soil organic carbon, OC = Oxidizable organic carbon, BC =Black carbon, POC = particulate organic carbon, PON = particulate organic nitrogen, LFOC = labile fraction organic carbon, and LFON = labile fraction organic nitrogen. ZTR = Zero tillage with residue retention, ZTWR = Zero tillage without residue retention; RTR = Reduced tillage with residue retention, RTWR = Reduced tillage without residue retention, CTR = Conventional tillage with residue incorporation; CT = Conventional tillage without residue incorporation.
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    The soil characteristics such as plant available nutrients, microbial diversity and soil organic carbon transformation are dwindling on account of intensive cultivation under conventional tillage practices, therefore, demand relevant management approaches for soil and crop sustainability. Long-term application of organic amendments significantly increases soil properties by increasing plant available macro, micro, secondary nutrients and soil organic C, whereas the increase in organic C by INF application is, by and large, due to increment in organic C content within macro-aggregates and in the silt + clay compartments. The soil organic carbon and other plant available nutrients were significantly greater in conservation tillage systems as compared to conventional tillage (CT) that conservation approaches could be an exemplary promoter of soil productivity by modifying soil structure thereby protecting SOM and maintaining higher nutrient content. The mean concentration of different fractions of carbon MBN, PMN and soil respiration under integrated nutrient management treatments was higher as compared with to control. Therefore, the conjoint use of organic manures or retention of crop residues with inorganic fertilizers is imperative to reduce the depletion of SOC while sustaining crop production as a realistic alternative. Future research should focus mainly on the usage of organic and mineral fertilizers in conjunction with conservation tillage approaches to sustain the soil environment.

    The authors confirm contribution to the paper as follows: study conception and design: Dhaliwal SS, Shukla AK, Randhawa MK, Behera SK; data collection: Sanddep S, Dhaliwal SS, Behera SK; analysis and interpretation of results: Dhaliwal SS, Gagandeep Kaur, Behera SK; draft manuscript preparation: Dhaliwal SS, walia, Shukla AK, Toor AS, Behera SK, Randhawa MK. All authors reviewed the results and approved the final version of the manuscript.

    Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

    The support rendered by the Departemnt of Soil Science, PAU, Ludhiana, RVSKVV, Gwailor, CSSRI, Karnal, IISS, Bhopal, School of Organic Farming, PAU Ludhiana and Washington State University, USA is fully acknowledged .

  • The authors declare that they have no conflict of interest.

  • Supplementary Table S1 WGA genomes of BIK2, BIK3, BIK4.
    Supplementary Table S2 GGDC of BIK2, BIK3, BIK4.
    Supplementary Table S3 DFAST annotation of BIK2, BIK3, BIK4.
    Supplementary Table S4 FACoP of BIK2, BIK3, BIK4.
    Supplementary Table S5 PhenDP of BIK2, BIK3, BIK4.
    Supplementary Table S6 VRprofile of BIK2, BIK3, BIK4.
    Supplementary Table S7 CRISPRloci of BIK2, BIK3, BIK4.
    Supplementary Table S8 antiSMASH of BIK2, BIK3, BIK4.
    Supplementary Table S9 Effective DB of BIK2, BIK3, BIK4.
    Supplementary Table S10 PLaBAse of BIK2, BIK3, BIK4.
    Supplementary Table S11 TAFinder of BIK2, BIK3, BIK4.
    Supplementary Table S12 Misa-BatchPrimer-3 of BIK2, BIK3, BIK4.
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  • Cite this article

    Barbadikar KM, Attal N, Vanama S, Pesari M, Kattupalli D, et al. 2024. Benign Bacillus: decoding the genetic potential of native rhizosphere Bacillus spp. from rice, to induce plant growth and defense. Technology in Agronomy 4: e032 doi: 10.48130/tia-0024-0028
    Barbadikar KM, Attal N, Vanama S, Pesari M, Kattupalli D, et al. 2024. Benign Bacillus: decoding the genetic potential of native rhizosphere Bacillus spp. from rice, to induce plant growth and defense. Technology in Agronomy 4: e032 doi: 10.48130/tia-0024-0028

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ARTICLE   Open Access    

Benign Bacillus: decoding the genetic potential of native rhizosphere Bacillus spp. from rice, to induce plant growth and defense

Technology in Agronomy  4 Article number: e032  (2024)  |  Cite this article

Abstract: Three bacterial strains namely Bacillus velezensis Strain BIK2, B. cabrialesii Strain BIK3, and B. paralicheniformis Strain BIK4, were extracted from indigenous rice soils in India. These strains demonstrated potent efficacy against major pathogens while stimulating plant growth in rice. Their genomic analysis indicated a rich array of genes associated with secondary metabolite production, plant growth promotion, elicitation, and biocontrol activities. Biosynthetic gene clusters having different classes of secondary metabolites surfactin, macrolactinH, bacillaene, fengycin, difficidin, bacillibactin, and bacilysin were identified using various online tools. This information may be used as template for identification of novel bioactive antibacterial, antifungal, and organic plant growth-promoting compounds. Toxin-antitoxin gene pairs identified could play roles in their antibiotic resistance and prevention of harmful deletions in the bacterial genomes. CRISPRs detected in these isolates offer prospects for future gene editing and patenting endeavors. Notably, the genomic profiles of BIK2, BIK3, and BIK4 underscore their emphasis on plant growth enhancement, evident through the presence of genes facilitating nitrogen fixation, phosphate, and potassium solubilization, and siderophore production. This comprehensive genomic insight paves the way for tailored Bacillus strains, facilitating the industrial production of efficacious biomolecules for enhancing plant growth, controlling pathogens, and advancing pharmacological applications.

    • Intensive agricultural practices driven by burgeoning population has led to extensive use of chemical fertilizers and pesticides causing severe environmental pollution Crop loss due to diseases coupled with evolving pesticide resistance and frequent breakdown of host plant resistance are major problems all over the world warranting an alternative strategy for the management of the evolving pathogens[1,2]. This necessitates a strategy that could manage plant growth and alleviate stress with minimum damage on environment and humans. Antagonistic and phyto-stimulant activities by micro-organisms called plant growth promoting rhizobacteria (PGPR), currently including rhizosphere fungi too, resident in the soil may hold a great promise for sustainable agriculture[3]. Selective manipulation and augmentation of these specialized microbial societies offer a successful strategy to manage plant diseases and improve plant growth in the most sustained and eco-friendly manner[4]. These rhizobacteria exert antimicrobial activity by direct parasitism, secreting bioactive molecules suppress disease-causing pathogens and sometimes serve as antibiotics, vitamins, and other molecules of industrial importance[5]. Bacterial strains from native rice soils of India viz., B. velezensis Strain BIK2, B. cabrialesii Strain BIK3, and B. paralicheniformis Strain BIK4 used in the study have shown dominant antagonistic properties[6,7], improving soil[8] and improving plant vigor, enhancing root and shoot growth in rice[7,9]. These bacteria exist as both free-living and endophytic in rice, resistant to several antibiotics, and compatible with an effective biocontrol agent, Trichoderma asperellum[8].

      Bacillus spp. are a class of endospore-forming, gram-positive bacteria that have evolved to produce various potent secondary metabolites essential for their survival[10]. Their significance in agriculture lies in their ability to form highly resilient endospores, facilitating their storage as dry powders with extended shelf lives[11]. The secondary metabolites they produce play a vital role in improving plant growth, stimulate plant immune response against phytopathogens, and facilitate soil nutrients available to plants[12]. B. cabrialesii and B. velezensis are effective, free living or endophytic bacterium that promote plant growth and antagonistic against many plant pathogens and nematodes[11,13,14]. While B. paralicheniformis, akin to B. licheniformis, finds industrial applications in enzyme and antibiotic synthesis, as well as various biochemical and consumer products[10,15].

      Thorough in-vitro, in-vivo, and field trials of BIK2, BIK3, and BIK3 strains have demonstrated significant plant growth-promoting and biocontrol effects against major rice pathogens such as Rhizoctonia solani, Ustilaginoidea virens, Sclerotium oryzae, and Xanthomonas oryzae pv. oryzae. These native strains have been extensively tested across various field conditions, that includes institute trial and farmers' fields. These studies have consistently shown the biocontrol efficiency of the isolates in the field conditions, supporting the practical applicability of these bioagents in diverse agricultural environments[8,9,16]. In this study, the focus is on whole genome comparison of these three native strains viz., BIK2, BIK3, and BIK3 in terms of their genome composition, virulence and antibiotic resistance, production of secondary metabolites, and their potential benefits to facilitate plant growth, resistance against plant pathogens and tolerance against abiotic stress. With the use of sophisticated bioinformatics tools, post-sequencing analysis of a large amount of data generated provides a meaningful interpretation of DNA sequence. This manuscript aims to answer the following questions:

      What are the genetic probabilities of these bacterial strains in producing secondary metabolites of agricultural and other uses?

      What are the target genes in native bacterial isolates for establishing beneficial relationships with host rice plants?

      Do the effectors in the three isolates have unique sequences as compared to other reported isolates that may help as biomarkers?

      Can modern bioinformatics tools help identify the phenotypic prospects from the genetic information available?

    • The overall workflow for the current study is described in Fig. 1.

      Figure 1. 

      Overall workflow of the BIK2, BIK3, and BIK3 analysis.

    • All the three native isolates of Bacillus were obtained from the culture collections maintained by this group at the ICAR- Indian Institute of Rice Research, Hyderabad, India. The cultures were recorded for their phyto-stimulant and biocontrol activities in rice over 3 years[79]. The strains were morphologically characterized and whole genome sequenced using genomic DNA. DNA isolation kit NucleoSpin® microbial DNA kit was used for DNA extraction as per the manufacturer's protocol (Macherey-Nagel, Germany), DNA libraries processed using standard protocols and sequenced using the HiSeq 2500 instrumentation platform (Agri Genome Labs Private Limited, Kochi, India)[7].

    • The Next-generation sequencing platforms generate vast datasets, with Illumina being a prevalent choice for sequencing and deciphering microbial genomes and being the prevalent platform. Assembly sequences of Bacillus velezensis Strain BIK2 (GenBank assembly accession number GCA_019336145.1), Bacillus cabrialesii Strain BIK3 (GCA_018829645.1), and Bacillus paralicheniformis Strain BIK4 (GCA_019336205.1) were obtained from the NCBI genome portal. Additionally representative sequences for corresponding species i.e., B. velezensis JS25R (GCF_0000769555.1), B. cabrialesii TE3 (GCA_004124315.2), and B. paralicheniformis Bac84 (GCA_002993925.1) were obtained for assessment. By using the online software REALPHY (Reference sequence Alignment based Phylogeny builder) another phylogenetic tress was built with the whole genome sequence datasets of seven Bacillus species other than our three datasets [Bacillus amyloliquefaciens (GCF_022559645.1), Bacillus subtilis (GCF_002055965.1), Bacillus licheniformis (GCF_022630555.1), Bacillus velezensis (GCF_002117165.1), Bacillus cabrialesii (GCF_032461835.1), Bacillus paralicheniformis (GCF_002993925.1), Bacillus cereus (GCF_002220285.1)]. The web-based tool was run with the default parameters that can infer phylogenetic trees from whole genome sequence data. From these alignments multiple sequence alignments will be reconstructed from which phylogenetic trees are inferred via PhyML[17].

    • Genome finishing generates complete and accurate representation of the genome of an organism. CONTIGuator (https://contiguator.sourceforge.net/)[18] is the widely used bioinformatics tool that orients the contigs based on a reference genome to infer relative positions of each contig in the draft genome by BIK2, BIK3, and BIK4 draft genomes along with their corresponding reference genomes in fasta format were analyzed with the default parameters.

    • Mauve is a powerful software tool designed for whole-genome alignment, facilitating the comparison of orthologous and xenologous regions among two or more genome sequences, even in the presence of extensive local and large-scale changes[19]. This alignment method enables the identification of evolutionary changes in DNA by aligning homologous regions of sequences and identifying their match, rearrangements, and variations. Fasta sequences with a match seed weight of 15, ideal for genomes around 5 MB is set which for analysis of BIK2, BIK3, and BIK4 with reference genomes and other complete genomes (Supplementary Table S1) submitted as input for alignment. By default, a full alignment and iterative refinement option was set for detailed analysis (applies MUSCLE 3.6).

    • GGDC web server (https://ggdc.dsmz.de/ggdc.php#)[20] compares the G+C content differences from the species with DNA-DNA Hybridization (DDH) similarities by maintaining a threshold level of 70% similarity between the species boundaries. This web service is used for genome-based species and subspecies delineation. In addition, the GGDC reports the difference in G+C content, which can also be reliably used for species delineation (Supplementary Table S2).

    • Genome sequence files of BIK2, BIK3, and BIK4 scaffolds were submitted in fasta format to the pipeline for further annotation and checked for completeness using 122 Bacillus genomes with 170 markers set. The DFAST (https://dfast.ddbj.nig.ac.jp/) stands for (DNA database of Japan) DDBJ Fast Annotation and Submission Tool was used for this analysis[21]. MetaGeneAnnotator for CDS, Barrnap for rRNA, Aragorn for tRNA, and CRT for CRISPR were used for the structural annotation used. The pipeline analysis resulted in fasta files for genomic, rRNA, tRNA, and protein sequences along with annotation and features files. In addition, FACoP (FACoP (molgenrug.nl)), a supporting system for FUNAGE-Pro[22] to classify genes for Gene Set Enrichment Analysis, was deployed with the supported classes GO, InterPro (IPR), KEGG-orthology KO, KEGG-pathways, eggNOG, COG, and PFAM. Protein sequences obtained from the DFAST annotation file were given as input to the server to perform enrichment analysis and classification (Supplementary Table S3 & S4).

    • GC skew is the difference between guanine (G) and cytosine (C) content in a DNA sequence and is anlyzed using GenSkew. GenSkew is used for identifying potential operons in bacterial genomes based on the analysis of GC skew. Sequences of BIK2, BIK3, and BIK4 scaffolds in fasta format were given as input to WebSkew (https://genskew.csb.univie.ac.at/webskew). The output was obtained in a tabular and graphical form. The global minimum and maximum are displayed in the cumulative graph. GenSkew identifies regions where the skew undergoes significant changes, and these changes are indicative of the boundaries of potential operons. The upper and lower bounds of GC-skew can be used to predict the origin of replication (minimum) and the terminus location (maximum) in prokaryotic genomes.

    • PhenDB (https://phendb.org/) is a bacterial trait identification interface based on comparative genomics, first predicts protein-coding genes in the given genome and then checks the completeness of the genome along with the prediction of marker genes in the orthologous groups of proteins (ENOGS) followed by the trait prediction using PICA. The submission form was filled with the scaffolded sequences of BIK2, BIK3, and BIK4 in fasta format for the prediction of phenotype. We used 0.75 as the balanced accuracy cut-off and 0.6 as confidence cut-off for predictions (Supplementary Table S5).

    • Diverse mobile genetic elements in our bacterial genome in in fasta sequence were identified using VRprofile2 (https://tool2-mml.sjtu.edu.cn/VRprofile/)[23]. This helped us to predict mobilome, possible mobilome interactions, and bacteria-mobilome-antibiotic resistance genes (ARGs) relationships. The pipeline includes the prediction of integron using IntegronFinder, SCCmec detection using SCCmecFinder, BLASTp for transposase (TnpA) and resolvase (TnpR) searches against TnCentral and TnRegistry databases (Supplementary Table S6).

    • In silico characterization of CRISPR-Cas system on bacterial genomes is essential for understanding adaptive immunity. The CRISPRloci (https://rna.informatik.uni-freiburg.de/CRISPRloci/Input.jsp) provides an automated and comprehensive in silico characterization of CRISPR-Cas system on bacterial genomes including CRISPR array orientation, detection of conserved leaders, Cas gene annotation, and subtype classification[24]. The scaffold genomic sequences of BIK2, BIK3, and BIK4 in fasta format were submitted to the server to identify the CRISPR-Cas genes. The server confirms the completeness of the genome and was set to identify the following viz., IS elements, degenerated repeat candidates on both ends of the CRISPR array candidate with range of 21 and 55 for repeat length and range of 18 and 78 for spacer length in the predicted array. To predict the Cas genes, ERT was used to assign the subtype of the identified CRISPR cassettes as well as to estimate the normalized bit scores of potentially missing proteins in the identified CRISPR cassettes (Supplementary Table S7).

    • The bacterial version of antiSMASH (Antibiotics & Secondary Metabolite Analysis Shell) (https://antismash.secondarymetabolites.org/#!/start)[25] was used under strict detection mode for identification and annotation, of genes and gene clusters of secondary metabolites in BIK2, BIK3, and BIK4 genomes. It integrates and cross-links with a large number of in silico secondary metabolite analysis tools (Supplementary Table S8).

      For additional information, the BAGEL4 webserver (http://bagel5.molgenrug.nl/) (BActeriocin GEnome mining tool)[26] was used to extract bacteriocins and other ribosomally synthesized and post-translationally modified proteins from BIK2, BIK3, and BIK4 genomes given fasta sequences as input.

    • The amino acid sequences of secondary metabolites in fasta format were used to search for homologous sequences and based on their structures, allowed us to predict the structure model of our molecules. SWISSModel (https://swissmodel.expasy.org/) which is a homology-based structure prediction server is used of this analysis. The models were evaluated by the server for structure quality and QMEAN scores were considered for model evaluation.

    • The amino acid sequences of all the secondary metabolites from BIK2, BIK3, and BIK4 in fasta format were submitted to the DisoRDPbind webserver (http://biomine.cs.vcu.edu/) that predicts RNA-, DNA-, and protein-binding residues located in the intrinsically disordered regions in a given protein sequence. The server performs analysis based on the information extracted from the physiochemical properties of amino acids, sequence complexity, putative structure and disorder, and sequence alignment.

    • EffectiveDB (http://effectivedb.org) is an online reference library that contains pre-calculated information of bacterial-secreted proteins and intact secretion systems[27]. This includes various tools to recognize Type III secretion signals, conserved binding sites of Type III chaperones, Type IV secretion peptides, eukaryotic-like domains, and subcellular targeting signals in the host. Protein sequences of BIK2, BIK3, and BIK4 in Fasta format were input to the submission form. Effective T3 was allowed to predict Type III secreted proteins based on their signal peptide with a minimal score of 0.9999 and T4SEpre (beta) for Type IV secreted proteins on the amino acid-based C-termini composition with a minimum score of 0.5. Effective CCBD Type III secreted protein on their secretion was allowed with a chaperon binding site. Predator was enabled to predict the subcellular localization of secreted proteins in plants (Supplementary Table S9).

    • Plant-interacting bacterial proteins were identified using the PIFAR module from PLant-associated BActeria web resource (PLABase)[28] using blastp + hmmer against the PIFAR protein collection and classification. Annotation of bacterial plant growth-promoting traits (proteins) 'PGPTs' was performed using blast (relaxed mode) and blastp + hmmer (strict) or IMG-KEGG-annotation mapping against the PGPT ontology. The protein sequences of BIK2, BIK3, and BIK4 genomes were submitted to identify the molecules involved in interaction with plants and show plant growth-promoting activity (Supplementary Table S10).

    • Toxin-antitoxin (TA) systems are the pairs of genes in a toxin system where in a stable toxin impedes the host cell growth by interfering with basic cellular processes and a corresponding unstable antitoxin in the host hampering the toxin activity. The reference TAfinder web server was used (https://bioinfo-mml.sjtu.edu.cn/TAfinder/TAfinder.php), which is designed to quickly predict and compare type II TA loci in newly sequenced bacterial genomes. It combines a homologous search module and an operon detection module to enhance the prediction performance. Scaffold sequences of BIK2, BIK3, and BIK4 in fasta format were submitted to identify the pairs with the parameters set as e-value for blast-0.01, e-value for HMMer-1, the maximum length of potential toxin/antitoxin-30 amino acids with maximum distance/overlap of −2 to 150 (Supplementary Table S11).

    • Proksee (https://proksee.ca) converts raw bacterial sequence data into whole-genome assemblies for description and interpretation. Sequences of BIK2, BIK3 BIK4 and their reference genomes JS25R, TE3, and Bac84 respectively in fasta format were submitted as input for displaying features as mobile genetic elements detected by mobileOG-db and categorized as integration/excision, replication/recombination/repair, transfer, stability/transfer/defense, and prophage-specific processes. In addition, a separate track was set to display putative Horizontal Gene Transfer (HGT) events as predicted by Alien Hunter.

    • MISA v2.1 was employed to generate SSR markers[29] for the BIK2 genome, while Primer3 was utilized for primer design. The process involved running the MISA Perl file alongside the BIK2 CONTIGuator fasta sequence via the command line, resulting in the creation of a statistics file and an SSR file for the BIK2 genome. Minimum numbers of repeats were set as 6, 5, 4, 3, and 3 for unit sizes 2, 3, 4, 5, and 6, respectively.

      BatchPrimer3[30] was used to find the possible generic primers for BIK2 with a product size of minimum 200 and maximum 350, primer size of minimum 18 and maximum 22 nucleotides, primer temperature is a minimum of 58 and maximum 60 degrees Celsius and primer GC% as the minimum of 40 and maximum of 45 (Supplementary Table S12).

    • The BIK2 draft assembly sequence consisted of 36 contigs, BIK3 with 49 contigs, and BIK4 with 56 contigs. These draft genomes were mapped against their respective reference genomes, which featured chromosome-level assemblies. Among these mappings, 18 were short and unmapped contigs, and four contigs exhibited duplication out of 22 for the BIK2 genome, while the remaining 15 were successfully mapped to reference genome (Fig. 2). For BIK3, 32 contigs were short and unmapped, and two contigs showing duplication and 1 contig showing poor coverage out of 35 total and the remaining 21 were potentially mapped to the reference genome (Fig. 3). Similarly, for BIK4, 22 contigs were short and unmapped, with two contigs exhibiting duplication out of 28 total, and the remaining 21 were mapped to the reference genome (Fig. 4).

      Figure 2. 

      Circular representation of BIK2 genome (innermost ring) along with reference genome B. velezensis JS25R. Putative Horizontal Gene Transfer (HGT) events were Predicted using Alien Hunter and are represented in green. The reference genome JSR is presented in teal, GC skew+ in green, and GC screw− in purple. Legends of genes in yellow represent the prophage genes, mustard yellow represent transfer-related mobile elements. Pink legends are for replication/recombination/repair genes, likewise.

      Figure 3. 

      Circular representation of BIK3 (innermost ring) along with reference genome B. cabrialesii TE3. Putative Horizontal Gene Transfer (HGT) events were predicted using Alien Hunter and are represented in maroon. The reference genome TE3 is presented in teal, with GC skew, and mobile genetic elements.

      Figure 4. 

      Circular representation of BIK4 (innermost ring) along with reference genome B. paralicheniformis Bac84. Putative Horizontal Gene Transfer (HGT) events were predicted using Alien Hunter and are represented in green and prophage genes in yellow. The reference genome Bac84 is presented in teal, with GC skew, and mobile genetic elements.

      The finished genome of BIK2 was annotated with 59 tRNA genes with a calculated genome completeness of 99.41% and 46.5% GC content based on the coverages of single-copy orthologous gene markers. Annotation revealed about 3,743 coding sequences (CDSs), 69 tRNA, and 144 as pseudogenes for the BIK2 scaffold. Gene enrichment analysis categorized genes into various functional groups, 119 in cell wall/membrane/envelope biogenesis, 29 in cell motility, 64 in post-translational modification, protein turnover, and chaperones, 123 in inorganic ion transport and metabolism, 44 in secondary metabolites biosynthesis, transport, and catabolism, 408 with unknown functions, 85 in signal transduction mechanisms, 14 in intracellular trafficking, secretion, and vesicular transport, and 35 in defense mechanisms. Similarly, for BIK3, the finished genome annotation consists of 4,008 CDSs, 59 tRNA, and one rRNA gene along with 115 pseudogenes with a genome completeness of 99.41% with 0.59% of contamination and 44.2% GC content. Gene enrichment analysis delineated genes related to 22 genes under chromosome partitioning, cellular processing and signaling, 31 under cell motility, 145 under envelope biogenesis, 45 under defense mechanisms, 14 under intracellular trafficking, secretion, and vesicular transport, 36 under secondary metabolite biosynthesis, transport and catabolism, and 520 with unknown functions.

      The genome completeness for BIK4 was estimated to be 100% with no contamination. Annotation identified 4,507 CDSs, 2 RNA, 70 tRNA, and 184 pseudogenes accompanied with a GC content of 45.5%. Gene enrichment analysis highlighted about 34 proteins involved in envelope biogenesis, 22 in defense mechanism, eight in secondary metabolite biosynthesis, transport, and catabolism, and 32 in signal transduction mechanisms. About 162 proteins were poorly characterized or with unknown functions (Fig. 5).

      Figure 5. 

      Comparative analysis of BIK2, BIK3, and BIK4 major classes of proteins involved in different processes. PTS-post-translational modifications, protein turnover, and chaperones (bar diagram), shows the classification of BIK2, BIK3, and BIK4 proteins under some of the major processes.

      The presence of pseudogenes, which are nonfunctional broken gene fragments that are formed after ecological shifts or extreme population bottlenecks[31] enable us to understand the evolutionary forces that have acted upon, and their functional capacities encoded within the bacterial genome. The presence of 146 pseudogenes in BIK2 compared to 58 pseudogenes in the reference genome, 115 in BIK3 compared to 100 in the reference genome TE3, and 184 in BIK4 compared to 66 in the reference genome Bac84 reveal the fact that these may have originated evolutionarily by either the disruption of a reading frame or promoter regions by point mutations, frameshifts, or by the integration of transposable elements[32] (Table 1; Supplementary Table S1 & Table S3).

      Table 1.  Scaffolding and annotation summary of BIK2, BIK3, and BIK4 along gene enrichment analysis.

      Variables B. velezensis BIK2 B. cabrialesii BIK3 B. paralicheniformis BIK4
      Scaffolding with CONTIGuator Input contigs 37 (3,902,606 bp) 49 (4,113,954 bp) 56 (4,424,204 bp)
      Mappeda contigs 15 (3,887,215 bp) 21 (4,046,514 bp) 21 (4,405,346 bp)
      Unmappedb contigs 22 (15,391 bp) 28 (67,440 bp) 35 (18,858 bp)
      Unmapped: short contigs 18 (7,793 bp) 22 (5,737 bp) 32 (10,951 bp)
      Unmapped: poor coverage 0 4 (57,117 bp) 1 (3,632 bp)
      Unmapped: duplicated hits 4 (7,598 bp) 2 (4,586 bp) 2 (4,275 bp)
      Annotation of scaffolds N50 (bp) 3,888,615 4,048,514 4,407,346
      Completeness (BUSCO) 99.41% 99.41% 100%
      Gap ratio (%) 0.036003 0.049401 0.045379
      GC content (%) 46.5 44.2 45.5
      Number of CDSs 3,743 4,008 4,507
      Coding ratio (%) 89.5 88.9 87.9
      Number of rRNAs 0 1 2
      Number of tRNAs 59 59 70
      Pseudogenes 146 115 184
      Gene enrichment analysis GO terms 1,955c uniques
      (8,176d duplicates)
      2,262 uniques
      (10,047 duplicates)
      1,011 uniques
      (2,826 duplicates)
      COG categories 1,923 2,342 777
      Cellular processes and signaling 363 433 124
      Information storage and processing 367 435 186
      Metabolism 787 953 305
      Poorly characterised/ unknown functions 408 520 162
      a, Contigs those aligned to reference genome; b, Contigs not aligned to reference genome; c, Uniques are those genes involved in single activity d, Duplicates are those involved in multiple activities.
    • The GGDC genome service was used to identify sub-species delineation and calculate the intergenomic distances for BIK2 along with 25 other reference genomes. GGDC initially determines a set of Highly Scoring Pairs (HSPs) or MUMs between two genomes, calculates the distances from these sets, then converts these distances in percent-wise similarities, analogous to DDH. As the genomes of BIK2, BIK3, and BIK3 are incomplete, we relied on formula 2-based results that are sequence-based calculations rather than the gene content of a species, to interpret the results. Accordingly, the results indicated that the J01 isolate is closer to BIK2 with 99.1% similarity and 0.32% G+C difference. Similarly based on the DDH similarity of 91%, it was observed that the TSO2 genome is closer to BIK3 compared to TE3, with a difference of 0.23% in G+C content. In the case of BIK4, it was observed that Bac84 is closer with 94.7% DDH similarity and has a difference of 0.38 % G+C content (Supplementary Table S2).

    • The Proksee representation illustrates the comparison between the BIK2 genome and the reference genome JS25R, highlighting mobile genetic elements detected by the mobileOG_db. In BIK2 a total of 92 genes related to various functions were identified, five genes for integration/excision, 41 involved in replication/recombination/repair, 32 genes of prophage, three for stability/transfer/defense and 11 for transfer. The innermost ring represents the BIK2 genome compared with the JS25R genome and the gapsin the representation shows the dissimilarities between the genomes. Further detailed analysis of the BIK3 genome indicate the presence of 94 mobile genetic elements along with 32 HGTs Specifically nine genes were found to be responsible for integration/excision, 30 for prophage sequences, 38 for replication/recombination/repair, five for stability/transfer/defense, and 13, exclusively for the transfer of genes. Analysis of the BIK4 genome, estimated about 130 mobile genetic elements with 13 genes involved in the integration/excision, 46 in replication/recombination/repair, 49 as prophage sequences, seven for stability/transfer/defense, and 15 for transfer (Supplementary Table S4).

    • The skew line depicted in blue and the cumulative line in red. The blue figure displays the skew over the length of the genome, the X-axis is the position in the genome, and the Y-axis is the skew. The cumulative skew adds up all previous values to a specific position. It also displays the global minimum and maximum, which are shown in the graph by the two green lines. These values estimate the origin of replication at 0 and the terminus location at 1,831,248 in the BIK2 genome with a step size and window size of 3,888 (Fig. 6). For the BIK3 genome, the origin of replication is predicted to be at the position of 4,035,856 and termination at 1,902,560 with a step size and window size of 4,048 (Fig. 7). The origin of replication for the BIK4 genome is estimate to be at position 4,393,779 and termination at 1,983,150 with a step size and window size of 4,407 (Fig. 8).

      Figure 6. 

      Gen-Skew: predicting the origin of replication for BIK2. The above figure shows the cumulative line in red and the skew line in blue, with minimum as origin of replication and maximum as termination of replication. X-axis denotes the positions in genomes and Y-axis denotes skew. For BIK2, replication starts at 0 position and terminates at 1,831,248 position.

      Figure 7. 

      Gen-Skew: predicting the origin of replication for BIK3. The above figure shows the cumulative line in red and the skew line in blue, with minimum as origin of replication and maximum as termination of replication. X-axis denotes the positions in genomes and Y-axis denotes skew. For BIK3, replication starts at 4,035,856 position and terminates at 1,902,560 position.

      Figure 8. 

      Gen-Skew: predicting the origin of replication for BIK4. The above figure shows the cumulative line in red and the skew line in blue, with minimum as origin of replication and maximum as termination of replication. X-axis denotes the positions in genomes and Y-axis denotes skew. For BIK4, replication starts at 4,393,779 position and terminates at 1,983,150 position.

    • The PhenDB analysis indicated that BIK2, a Gram-positive bacterium is capable of aerobic respiration, with a fermentative lifestyle, capable of producing R_acetoin (a natural product) and with a Type IV secretory system. In addition, it is capable of self-propelled motion and can produce endospores for persistence. Data also shows that BIK2 may produce ethanol, formic acid, hydrogen, butyric acid, etc (Supplementary Table S5).

    • The VRprofile2 analysis revealed the presence of - antibiotic resistance genes cfr(B) and tet(L) in the BIK2 scaffold. Precisely, the cfr(B) spanning the regions 520,020−521,069 conferring resistance to drugs Chloramphenicol, Florfenicol, Clindamycin, Lincomycin, Linezolid, Dalfopristin, Pristinamycin & IIA, Virginiamycin & M, Tiamulin of the drug classes Oxazolidinone, Phenicol, Lincosamide, Streptogramin & A, Pleuromutilin. Meanwhile tet(L) occupies the regions of 2,486,195−2,487,571 and endows. Cfr (B) gene exhibits resistance to Doxycycline which belong to a tetracycline class. No virulence factors were detected for the BIK2 genome. IN the case of BIK3 genome, the VRprofile2 analysis showed the presence of genes mph (K) spanning the region 228,851−22,977 conferring resistance to Spiramycin, and Telithromycin belonging to the class of Macrolide, and aadK genes occupying the regions between 2,550,575 to 2,551,429 and conferring resistance to streptomycin belonging to the class aminoglycoside. No virulence factors were detected for the BIK3 genome. Five genomic islands were identified in the BIK3 genome, confirming the horizontal gene transfer events. In the case of BIK4 genome, a virulent gene clpE (ATP-dependent protease) was found to be associated with the mobile genetic element for Listeria monocytogenes EGD-e. The VRprofile2 also detected the erm(D) gene responsible for resistance to erythromycin from the Macrolide drug class, lincomycin belonging to Lincosamide, clindamycin, quinupristin, pristinamycin and IA belonging to Virginiamycin, Streptogramin and B (Supplementary Table S6).

    • CRISPRloci identified about 10 Cas proteins of CAS-VI-C class and 10 for CAS-VI-B class in BIK2, eight cas genes of subtype V-A, six of subtype V-F, and 21 of VI-B in the BIK3 genome and four cas genes with subtype V-A, three of V-B, and 12 of subtype VI-B in the BIK4 genome (Supplementary Table S7).

    • The BIK2, BIK3, and BIK4 genomes were subjected to whole genome alignment with their respective reference genomes and other completed genomes of corresponding species. In addition, the BIK2 genomic sequence was scaffolded along with 25 complete genomic sequences of B. velezensis in fasta formats. Results indicated that in the case of BIK2 genome the root alignment has 2,400 super intervals and the root alignment length was 6,265,618, 80 super intervals and root alignment length of 4,603,858 in BIK3 and 975 super intervals with 7,858,026 root alignment length in BIK4 (Fig. 9; Supplementary Table S1). The clustering pattern using REALPHY web-based tool showed that the three isolates grouped with their respective subspecies (Fig. 10).

      Figure 9. 

      Whole genome phylogenetic tree of BIK-2, 3 and 4. The tree is generated after whole genome alignment visualized with iTOL version 6.6. The isolates BIK-2, 3 and 4 are highlighted in different colours. Numbers on the branches denote the age of the node instead of raw branch length values. Farthest leaf in the tree has the age zero, and the age increases towards the root of the tree. Here, B. cabrialesii TE3 has node age 0, meaning that it is the farthest node. (The node age is restricted to three decimals).

      Figure 10. 

      Whole genome phylogeny using of beneficial Bacillus species using REALPHY programme. REALPHY web-based programme. Bacillus velezensis-BIK2 (GCF_019336145.1), Bacillus cabrialesii-BIK3 (GCF_018829645.1), Bacillus paralicheniformis-BIK4 (GCF_019336205.1), Bacillus amyloliquefaciens (GCF_022559645.1), Bacillus subtilis (GCF_002055965.1), Bacillus licheniformis (GCF_022630555.1), Bacillus velezensis (GCF_002117165.1), Bacillus cabrialesii (GCF_032461835.1), Bacillus paralicheniformis (GCF_002993925.1), Bacillus cereus (GCF_002220285.1). REALPHY uses phyML fast maximum likelihood methods for the analysis.

    • Bacterial version of the antiSMASH software was used to analyze the genomic locations in the bacterial genomes to estimate their ability to produce antimicrobial SM. Accordingly the analysis of the BIK2 scaffold revealed its potential to produce surfactin, macrolactinH, bacillaene, fengycin, difficidin, bacillibactin, and bacilysin, were located in the genome with 100 % similarity of known clusters. These SM, notably, Difficidin and bacilysin are well-known antibacterial agents which may favorably contribute for the for the biocontrol activity of the bacterial strain. The table describes the antiSMASH bacterial version results of B. velezensis BIK2 for the detection of secondary metabolite encoding clusters along with their genomic locations. Clusters with a threshold similarity of more than 70% were considered. Many transport-related, regulatory and other genes were identified in BCGs of secondary metabolites. Analysis using BAGEL4 resulted in the identification of a class II lanthipeptide lichenicidin, which is a novel circular bacteriocin-amylocyclicin, competence pheromone ComX, and an antimicrobial peptide LCI. Along with several other NRPS and PKS compounds, RiPP molecules in the strain BIK3. Similarly, the SM compounds antibiotic dehydratase, subtilosin_A, competence pheromone ComX, and colicin. BAGEL4 identified sonorensin, enterocin, Fengycin and competence pheromone ComX were detected from the strain BIK4. Refer to Tables 24, Supplementary Table S8 for more details.

      Table 2.  antiSMASH bacterial version results of B. velezensis BIK2.

      Type Biosynthetic class Location Most similar known cluster Similarity (%)
      transAT-PKS Polyketide 1,349,542−1,437,358 Macrolactin H 100%
      transAT-PKS, T3PKS, NRPS Polyketide + NRP 1,656,859−1,757,461 Bacillaene 100%
      NRPS, transAT-PKS, betalactone NRP 1,822,672−1,948,610 Fengycin 100%
      NRPS, RiPP-like NRP 2,974,339−3,026,132 Bacillibactin 100%
      Other Other 3,541,315−3,582,733 Bacilysin 100%
      transAT-PKS Polyketide + NRP 2,236,311−2,326,509 Difficidin 100%
      NRPS NRP 302,447−378,024 Surfactin 82%

      Table 3.  Secondary metabolites identified by antiSMASH bacterial version for B. cabrialesii BIK3 scaffold.

      Type Biosynthetic class Location Most similar known cluster Similarity (%)
      NRPS NRP: Lipopeptide 314,344−378,335 Surfactin 86%
      TransAT-PKS, T3PKS, NRPS Polyketide + NRP 1,718,221−1,823,173 Bacillaene 100%
      NRPS, transAT-PKS, betalactone NRP 1,893,277−2,016,972 Fengycin 100%
      NRPS NRP 3,046,631−3,093,767 Bacillibactin 100%
      Sactipeptide RiPP: Thiopeptide 3,643,594−3,665,205 Subtilosin 100%
      Other Other 3,668,257−3,709,675 Bacilysin 100%

      Table 4.  Secondary metabolites identified by antiSMASH bacterial version for Bacillus paralicheniformis BIK4 scaffold.

      Type Biosynthetic class Location Most similar
      known cluster
      Similarity
      (%)
      NRPS NRP 334,165−396,222 Lichenycin 100%
      NRPS, betalactone NRP 2,028,578−2,099,
      311
      Fengycin 86%
      Molecules having less than threshold % similarity were discarded.
    • Class 1B chaperone prediction assisted estimation of Type III secreted proteins follows the explicit pattern (LMIF)1XXX(IV)5XX(IV)8X(N)10 according to Costa et al.[33]. The server analysis for strain BIK2 predicted 97 proteins with conserved binding sites to chaperones, 23 of which were located within the N-terminal regions of secreted proteins and additionally, the secretion of 180 type III molecules. For BIK3, the analysis Effective T3 from Effective DB revealed the presence of 198 secreted type III proteins and the identification of 82 proteins with conserved binding sites to chaperones with 28 within the N-terminal region. The BIK4 genome was found to code for 74 secreted type III proteins. 19 proteins having conserved binding sites for chaperones along with 14 being within the N-terminal region were detected (Supplementary Table S9).

    • Analysis of the genomes indicated that about 26% of the BIK2 genome is predicted to be involved in colonizing plant systems, 22% in stress and biocontrol activity, 21% in competitive exclusion, 12% in biofertilization, 10% in plant signal production, and 2% in plant immune response stimulation. It was further estimated that 39% of the BIK2 genome produces toxins, 21% extracellular polymeric substances, 7% in detoxification, 1% in plant cell wall degrading enzymes production, and 1% in volatile production. Notably, the BIK2 genome encodes for siderophores such as equibactin, mycobactin, petrobactin, coprogen, and rhizobactin along with Bacillibactin and Enterobactin. 36 genes are found to be involved in nitrogen acquisition, 80 genes in phosphate solubilization, 67 genes in potassium solubilization. Several genes such as iscR, pstA, pstC, arsB, arsC1, czcD, chrR, etc. are found to be present in BIK2, exhibiting resistance to heavy metals such as arsenic, chromate, cobalt, bismuth, copper, cadmium, and iron, etc. In all, 622 genes were identified as responsible for colonizing plant systems including attachment, adhesion, cell wall degradation (amyA, abnA, xynD, lacG, celJ, sacA, treC, xynC, srfJ, etc.), and invasion in the plant cell. 53 genes encoding toxic compounds for competitive exclusion exhibit resistance to surfactin, tetracycline, rifamycin, quinolone, etc. were identified. Seventy-two genes including regulators, transporter, quorum sensing, etc. are found to be involved in biofilm formation. BIK2 genes as bpsA and bpsB, involved in alkylresorcinol are found to have antiprotistal activity. Along with bactericidal molecules bacillaene, bacitracin, difficidin, and fengycin, predicted by antiSMASH and BAGEL4, PGPT-Pred can predict molecules such as nisin, plipastatin, cycloserine (antibiotic), cephaloporin (antimicrobial activity), toxoflavin (antifungal, antibiotic, phytotoxin), tylosin (macrolide antibiotic), spermidine and tetracycline. BIK2 genes encoding fengycin, bacillimycin, ansamitosin, and alkylresorcinol were found to have fungicidal activity. Genes such as gabP, gabT, and puuE involved in gamma-aminobutyric acid biosynthesis were found to have insecticidal activity. 716 genes/proteins from BIK2 were found to have a direct effect on plants, 1,924 indirect effects, and seven being predicted as putative plant growth-promoting molecules (Fig. 11).

      Figure 11. 

      Distribution of BIK2 proteins. The figure explains PIFAR-Pred annotation and classification of BIK2 proteins interacting with host plants. Annotations are obtained from PIFAR protein collection after blastp + hmmer analysis.

      Similar analysis of BIK3 genome indicated that 28% of its total genome is involved in colonizing plant systems, 22% in competitive exclusion, 21% in biocontrol and stress control activity, 10% in plant signal production, 11% in the biofertilization process, 2% in plant immune response stimulation. It was predicted that 35% of the BIK3 genome produces toxins, 25% extracellular polymeric substances, 2% plant cell wall degrading enzymes, 1% volatile substances, 8% detoxifying enzymes, 2% enzymes required for adhesion to plant, and 2% for movement. For BIK3, proteins involved in xenobiotic transport and production of bactericidal compound nisin were the most frequent class followed by siderophore Bacillibactin production, prodigiosin production, biotin biosynthesis (Vitamin B7) required for root colonization. Along with siderophores Bacillibactin and Enterobactin, coprogen, and mycobactin were predicted to be involved in iron acquisition. BIK3 53 genes were involved in nitrogen acquisition including genes involved in allantoin metabolism (allB, allC), glutamate transport (TC_AAT/yifK, gltP, ntrA), nitrogenase biosynthesis (nifS, nifU), and many other transports and regulatory molecules. BIK3 118 genes were predicted to be involved in phosphate solubilisation, whereas 101 in potassium solubilisation. Two genes TC_CIC|eriC and crcB were found to exhibit resistance to fluoride. 134 genes were found to exhibit resistance to heavy metals such as copper, nickel, manganese, tellurium, tungstate, lead, zinc etc. 29 cell wall degrading enzymes with many other cell attachment, adherence and invasion proteins were identified. 13 genes involved in niacin biosynthesis and 16 genes involved in biotin biosynthesis were found to be needed for root colonization. Genes such as glpT, glmS, maa|nodL, glpA|glpD, glpK and nodX were found to colonize roots by nodulation. Genes exhibiting resistance antimicrobial compounds included resistance to bacitracin, beta-lactam, bleomycin, catechol, bacitracin, chromanon, Lincomycin, fosfomycin etc. BIK3 128 genes were predicted to be involved in biofilm formation including several transporters, regulators, and signaling molecules. 45 proteins involved in thiamin (vitamin B1), riboflavin (vitamin B2), and proteins involved in 3-BUTANEDIOL_BIOSYNTHESIS were found to induce systemic resistance (ISR). 22 proteins were found to trigger PAMP response in plant. Genes bpsB, and bpsA products involved in alkylpyrone biosynthesis were predicted to have antiprotistal and bactericidal activity. Along with difficidin, bacillaene etc, molecules as auracin, spermidine, nisin, prodigiosin, nocardicin A−D (beta-lactam antibiotic), cycloserine etc were predicted to have bactericidal activity. BIK3 814 proteins were predicted to have direct effect on plants, 2,242 indirect effects with eight molecules being predicted to have PGPR activity. Presence of several fungicidal molecules were predicted to be involved in mycosubtilin metabolism, toxoflavin metabolism, fungal glycogen degradation, motility-mediated defence signaling etc. (Fig. 12).

      Figure 12. 

      Distribution of BIK3 proteins. The figure explains PIFAR-Pred annotation and classification of BIK3 proteins interacting with host plants. Annotations are obtained from PIFAR protein collection after blastp + hmmer analysis.

      The analysis of BIK4 genome estimated that about 29% of the genome is to be involved in colonizing the plant system, 22% in biocontrol and stress control activity, 21% in competitive exclusion, 9% in biofertilization, 9% in plant signal production, 8% in bioremediation, and 2% in plant immune response stimulation. It was predicted that 38% of BIK4 genome is involved in toxin production, 16% in extracellular polymeric substance production (natural polymers required for biofilm structural and functional integrity), 5% in detoxification processes, 1% in volatiles production and plant cell wall degrading enzymes. BIK4 proteins interacting with plants were annotated and classified using PIFAR-Pred and PGPT_Pred at six different levels with their frequencies (Fig. 13). Production of bactericidal compounds/antibiotics and xenobiotic degradation by transport of proteins were the most frequent molecules in plant interaction. Presence of siderophores such as Bacillibactin, coprogen, desferrioxamine, petrobactin and rhizobactin were predicted to interact with plant. Presence of fluoride resistance gene crcB along with genes exhibiting resistance to heavy metals such as antimony, arsenic, bismuth, cobalt, copper, iron, manganese, nickel, tellurium, zinc, and tungstate were predicted to be in BIK4 genome. The server annotated BIK4 genes bsdA, bsdC and ubiX responsible for colonization to plant by inhibiting the activity of plant hormone salicylic acid. Also, several cell wall degrading enzymes encoding genes such as amylase (amyA), arabinanase (abnA), carrageenase (celF), galactosaminidase (nagZ), galactosidase (bgaB, bglA, melA) etc. were identified. It identified genes such as iunH, nadE, npdA etc. involved in niacin biosynthesis (vitamin B3) and genes such as fabF, fabH, bioA, bioB etc. involved in biotin biosynthesis (vitamin B7) required for colonization at plant root. In all 57 genes were found to be involved in biofilm formation including biofilm regulators, transporters, and quorum sensing response proteins. BIK4 is also predicted to produce riboflavin (vitamin B2), 3-butanediol (volatiles) and thiamine (vitamin B1) responsible for stimulating induced systemic resistance in plants. Two genes namely elf18 (bacterial EF-TU) and srfATE (surfactin) are found to trigger PAMP responses in plant. Along with the secondary metabolites identified through BAGEL4 and antiSMASH, PLaBASE identified several bactericidal molecules such as ansamitosin (antimicrobial, antifungal and antitumor activity), nisin (antibacterial peptide), aklavinone (antineoplastic agent), mithramycin (antineoplastic antibiotic), tetracycline, tetracenomycin etc. Also, phenazine, natural bacterial antibiotic, is found to be present in BIK4 genome which might help protect plants from diseases. Gene encoding gamma-aminobutyric acid (gabT) is found to exhibit insecticidal activity. 315 BIK4 proteins were predicted to have direct effects on plant, 909 indirect effects and two proteins were predicted to have putative plant growth-promoting activity (Supplementary Table S10; Fig. 14).

      Figure 13. 

      Distribution of BIK4 proteins. The figure explains PIFAR-Pred annotation and classification of BIK4 proteins interacting with host plants. Annotations are obtained from PIFAR protein collection after blastp + hmmer analysis.

      Figure 14. 

      Histogram showing distribution of bacterial proteins interacting with plant. BIK2, BIK3, and BIK4 proteins are found to interact with plants in various ways. Molecules are classified under different stages of interaction.

    • Toxin-antitoxin systems have been classified into five types according to the molecular nature of the antitoxin and how it neutralizes the toxin. In the type II TA system, both the antitoxin and toxin are proteins and the neutralization is performed by forming a toxin-antitoxin complex. It shows that TA systems are related to the formation of persistence cells, stress resistance, regulation of biofilm formation, programmed cell death, and other biological process. Due to its contribution to genetic elements maintenance, TA systems could be extensively applied in genetic manipulation. For BIK2, the server predicted 15 pairs of TA systems. The pairs included the proteins from families COG2856like_domain, Xrelike_domain, mazF, and mazE. TAFinder resulted in no identification of the TA system in BIK3 and BIK4 genomes. PLaBASE, on the other hand, annotated 15 entries for TA systems in BIK3 and 8 entries for BIK4 (Supplementary Table S8). This suggests that PLaBASE, using its distinct algorithms or databases, identified potential TA systems in these genomes. The differences in results between TAFinder and PLaBASE could stem from variations in prediction methods, databases used, or the specific criteria for identifying TA systems. This could be due to the diversity of TA systems, variations in sequence motifs, or limitations of the prediction tool. Bacteria possess multiple mechanisms to cope with stress and ensure survival. While TA systems are one such mechanism, bacteria may rely on alternative systems, specific ecological niche of the bacterial strain or strategies to respond to environmental challenges (Supplementary Table S11).

    • Ten SSRs for the CDS sequence of BIK2 were identified with one trimer ATC/ATG, 4 pentamers, and the most frequent hexamer AGGCGG/CCGCCT. For BIK3, 12 SSR markers were identified for CDS sequences with ATC/ATG trimer, AAAAC/GTTTT frequent pentamer, and four hexamers. For BIK4, one SSR marker AACGG/CCGTT was identified as p5 SSR type starting from the region 184−198 for ABC_transporter_permease (Supplementary Table S9). Out of 10, primers were designed for seven protein sequences viz. cell wall binding protein YocH, stage III sporulation protein AF, alanine tRNA ligase, GTPase Obg, ESX secretion system protein YueB, aminopeptidase YwaD, and one hypothetical protein for BIK2 CDS. For BIK3 CDS, primers were designed for putative transporter YdbO, zinc-specific metallo regulatory protein, and one hypothetical protein. No primers were designed for BIK4 (Table 5; Supplementary Table S12).

      Table 5.  Frequency of identified SSR motifs in BIK2, BIK3, and BIK4.

      Parameters of SSR search BIK2 BIK3 BIK4
      Parameters CDS CDS CDS
      The total number of sequences
      examined
      3,743 3,326 1,238
      The total size of examined
      sequences (bp)
      3,481,005 2,998,222 1,108,330
      A total number of identified SSRs 10 12 1
      Number of SSR-containing sequences 10 12 1
      Trimers 1 3
      Pentamers 4 5 1
      Hexamers 5 4
    • Biotic and abiotic factors have always affected the activities of bacteria and in response, bacteria have adapted to these stresses by mutating their genomes in terms of loss and/or gains of genes, and mobile elements. The BIK4 genome has been shown to have more prophage genes compared to BIK2 and BIK3. Several horizontally transferred genes have been identified in the three species which could be predicted to show antibiotic resistance. Genome finishing is done for BIK2, BIK3, and BIK4 allowing the visualization of maps of contigs, underlining the loss and/or gain of genetic elements, and permitting to finish of multipartite genomes. BIK4 was noted to have virulence gene clpE for Listeria monocytogenes EGD-e. Difficidin and bacilysin-like molecules are proven to have antibacterial activity[34]. Bacilysin is also known to have anticyanobacterial activity against harmful alga Microcystis aeruginosa and thus could be used as a targeted biocontrol agent[11]. Phosphate and potassium solubilizing properties and nitrogen acquisition properties of BIK2, BIK3, and BIK4 make these species more beneficial to the plants. Macrolactins are mostly produced by marine microorganisms. These have unique structures and novel activities. Different types of macrolactins exhibit different potentials for antibacterial, antifungal, antiviral, anticancer, anti-inflammatory, anti-angiogenic (against cancer), and other activities. Bacterial siderophores perform several functions like they alter the microbial community in the soil, promoting plant growth, and enhancing the bioremediation of heavy metals. Volatile organic compounds (VOCs), acetoin, and 2,3-butanediol produced by these species could be predicted to suppress virulent microbes. Fengycin, from BIK2, BIK3, and BIK4 can be said to have antifungal activity and may prove beneficial to plants to survive against fungal attacks. Several secreted effector molecules were predicted for BIK2, BIK3, and BIK4 genomes.

      The genetic stability of the strains under different environmental conditions were checked rigorously by continuous application over two consecutive years across various field trials in different regions of India. The microbial strains were reisolated and compared with the initial load in the soil. The results have indicated an increase in the colony forming unit (CFU) content even in the following season in the same soils where the microbes were applied previously. This indicates that the microbial strains have better competitive saprophytic ability (CSA) to survive in the introduced soils. Furthermore, the strains have continuously shown robust antagonistic capacities throughout these studies, showing no evidence of genetic changes or diminished efficiency. This consistency across diverse environments indicates that these organisms are genetically stable and capable of maintaining their performance over long-term application[79]. The exploration of genomic features provides insights into the genetic composition, potential phenotypes, virulence factors, and defense mechanisms of the three bacterial genomes.

      The presence of antibiotic-resistance genes and the diversity in CRISPR loci highlight the adaptive strategies and evolutionary dynamics of these bacteria. Further research could explore the implications of these genomic features in environmental adaptation, host interactions, and bacterial evolution. Understanding the presence of mobile genetic elements, such as prophages and genes associated with replication/recombination/repair, provides insights into the evolutionary history of these bacteria. This knowledge aids researchers in tracing the genetic changes and adaptations that have occurred over time, contributing to our understanding of microbial evolution. The diversity of genomic features, including mobile elements and CRISPRloci, highlights the genomic plasticity and adaptability of these bacterial genomes. Microbial research can delve into the specific environmental cues that trigger genetic changes, providing insights into how bacteria adapt to different ecological niches. Predictions related to phenotype, such as metabolic capabilities and lifestyle traits, offer valuable information for researchers studying the ecological roles of these bacteria. The whole genome alignment and GGDC results emphasize the genomic diversity among the Bacillus isolates. The variations in alignment lengths and super intervals suggest that each isolate has unique genomic characteristics. The identification of close relatives and reference genomes provides information on their evolutionary relationships, shedding light on the genetic variations that have occurred over time. The presence of secondary metabolites, including antibacterial agents like difficidin and bacilysin, underscores the biocontrol potential of these Bacillus isolates. These compounds have known antimicrobial activities and can contribute to the inhibition of pathogenic organisms in agricultural settings. All these three strains were naturally isolated from the soil, and phenotypically shown antagonistic ability against the major rice pathogens such as Rhizoctonia solani, Ustilaginoidea virens, Sclerotium oryzae, and Xanthomonas oryzae pv. oryzae under in-vitro and in-vivo conditions. These results shed light on the effectiveness of these strains against wide-range of diseases[79,16]. The identification of bioactive proteins and secretory systems further supports their role in biocontrol strategies[3539]. Enterobactin is found to be responsible for inducing systemic resistance (ISR) in plants along with genes involved in thiamin biosynthesis (adk, dxs, phoA, rsgA, tenA, etc.), riboflavin biosynthesis (bluB, ribBA, ribD, ribE, ribF, yigB, etc.) and butanediol biosynthesis (acoA, acoB, acuA, acuB, budA, etc.). The gene entE involved in salicylic acid biosynthesis is found to induce systemic acquired resistance (SAR) in plants. Genes involved in flagellin-triggered immunity (fliC, flgK, fliD), surfactin production (srfAA, srfAC), teichuronic acid (tuaA, tuaB, tuaC, tuaE, tuaH, tuaG, wecA) are found to trigger PAMP response[40].

      The SSR markers mined from the genomic data can be deployed in population genetic and molecular diversity studies and evolutionary analysis in a large collection of isolates. These SSR primers can be deployed to generate unique SSR profiles for bacterial isolates for providing specific genetic fingerprints, determination, and characterization. Hence, the mined SSR markers have added to the repertoire of the data available for these beneficial isolates[41].

      The Bacillus strains used in the current study were isolated from natural soils and have been tested for their impact on plant growth and pathogen resistance in rice. These strains are safe for use in agriculture and are currently undergoing multilocation field trials in India to evaluate their efficacy and monitor potential ecological risks. The antibiotic resistance genes in these strains help individual microbes survive in soil and function efficiently. In addition to this the prediction of potential toxin-antitoxin systems in these genomes have led us to do more functional investigations and validation of their roles in the ecological adaptation and biocontrol potential of the strains in the future.

    • The genomic analysis of Bacillus isolates BIK2, BIK3, and BIK4 provides a comprehensive understanding of their genetic makeup and functional potential. It can be inferred based on the genomic features of the three isolates, BIK2, BIK3, and BIK4 employ diverse strategies, including toxin production, secondary metabolite synthesis, and biofilm formation, suggesting their versatility in environmental adaptation and plant-microbe interactions. The presence of genes involved in stress response, metal resistance, and biofilm formation indicates the adaptability of these Bacillus isolates to different environmental conditions. Harnessing these natural defense mechanisms could lead to the development of eco-friendly alternatives for disease and pest management in agriculture. The identification of novel secondary metabolites, antimicrobial compounds, TA systems, CRISPRs has implications for agriculture, biotechnology, and microbial ecology, contributing to ongoing efforts to harness the capabilities of beneficial bacteria for sustainable and resilient ecosystems.

      • Authors acknowledge the ICAR-Indian Institute of Rice Research for funding this work.

      • The authors confirm contribution to the paper as follows: study conception and design: Chinnaswami K, Barbadikar MB; data collection: Barbadikar MB, Chinnaswami K, Attal N; analysis and interpretation of results: Barbadikar MB, Attal N; draft manuscript preparation: Attal N; practical experiments and timely inputs: Vanama S, Pesari M; technical check: Kattupalli D; project supervision and critical comments: Sundaram RM; overall supervision and critical revisions: Chinnaswami K. All authors reviewed the results and approved the final version of the manuscript.

      • All data generated or analyzed during this study are included in this published article and its supplementary information files.

      • The authors declare that they have no conflict of interest.

      • # Authors contributed equally: Kalyani M. Barbadikar, Neha Attal

      • Copyright: © 2024 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
    Figure (14)  Table (5) References (41)
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    Barbadikar KM, Attal N, Vanama S, Pesari M, Kattupalli D, et al. 2024. Benign Bacillus: decoding the genetic potential of native rhizosphere Bacillus spp. from rice, to induce plant growth and defense. Technology in Agronomy 4: e032 doi: 10.48130/tia-0024-0028
    Barbadikar KM, Attal N, Vanama S, Pesari M, Kattupalli D, et al. 2024. Benign Bacillus: decoding the genetic potential of native rhizosphere Bacillus spp. from rice, to induce plant growth and defense. Technology in Agronomy 4: e032 doi: 10.48130/tia-0024-0028

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