Post translational modifications as biomarkers of soil microbe responses to nano-pesticides

Abstract

Nano-pesticides represent a significant technology advancement in modern agricultural, offering improved target specificity and reduced chemical load. However, their potential to induce subtle, sub-lethal disturbance in soil microbial function remains poorly resolved and is not adequately capture by conventional indicators such as microbial diversity, abundance, or bulk enzymatic activity. The central novelty of this review lies in proposing post-translational modifications (PTMs) as functional, early-warning biomarkers for nano-pesticide induced microbial stress, providing a molecular resolution that bridges exposure and ecological outcome. This review critically examines the current evidence on nano-pesticides-microbiome interaction and PTM-centric framework to interpret microbial responses at the protein regulation level. We highlight phosphorylation, acetylation, and ubiquitination regulate microbial stress responses, modulating detoxification enzymes, efflux pumps, and cellular signalling pathways under nanoparticle-induced stress. Unlike prior reviews that emphasize toxicity endpoints or gene-level responses, this work integrates metaproteomic evidence demonstrating PTM enrichment within stress-responsive functional protein groups across real environmental datasets, underscoring their relevance as conserved biomarkers of adaptive and maladaptive responses. By integrating metagenomics with metaproteomic and metabolomics, this review illustrates how PTM profiling enables mechanistic insight into microbial adaptation, functional impairment, and resilience under nano-pesticide pressure. Furthermore, we introduce a systems-level perspective that combines PTM data with computational modelling and AI-assisted bioinformatics to predict microbiome shifts and ecological risk, an approach not previously synthesized within the context of nano-pesticide assessment. Collectively, this review bridges nanomaterial design, microbial molecular regulation, and environmental risk evaluation, and proposes PTM-based assessment as a new paradigm for developing microbiome-safe, eco-compatible nano-pesticides and advancing molecular environmental monitoring strategies.

Graphical abstract

Introduction

Nano-pesticides have emerged as a transformative class of agrochemicals designed nanotechnology to enhance pesticide efficacy while reducing environmental impact. By incorporating active ingredient into nano-scale carriers or engineering nano-enabled actives themselves, these formulations improve adhesion, stability, targeted delivery, and controlled release, thereby addressing critical challenges associated with conventional pesticides like chemical runoff, and non-target toxicity [55, 111]. Advances in carrier surface chemistry using amphiphilic molecules like ethyl lauroyl arginate, have markedly improved leaf adhesion and retention, translating into reduction in pesticide loss and improve bio efficacy. For example, neem seed extract-loaded nano-carriers have shown significant increases in pest control efficiency, alongside measurable reduction in non-target toxicity [6]. These developments position nano-pesticides as promising tools for sustainable agriculture rather than simple reformulations of existing chemicals. Despite these advantages, the environmental fate and biological effect of nano-pesticides remain incomplete understood. While nano-formulation often exhibit reduced leaching and prolonged activity relative to conventional pesticide, there interaction with soil and aquatic microbiomes introduce new layer of complexity [104]. Nano-particles can directly or indirectly induce oxidative stress, alter enzymatic activities, and disrupt microbial metabolic pathways without necessarily causing immediate cell death [1, 78]. Such sub-lethal effects challenge traditional ecotoxicological frameworks, which largely rely on microbial abundance, diversity indices, or gross functional outputs. These approaches frequently fail to capture early or mechanistically informative molecular perturbations that precede observable ecological shifts. Therefore, there is an urgent need for biomarkers that accurately reflect the functional and regulatory state of microbial communities exposed to nano-pesticides.

Post-Translational Modifications (PTMs) offer a powerful yet underutilized molecular lens through which these subtle but consequential effects can be examined. PTMs, including phosphorylation, acetylation, ubiquitination, and redox based modifications enable microorganisms to swiftly modulate protein function, localization, stability, and interaction networks in response to environmental stress without necessitating changes at the genetic level [35]. Importantly, PTMs do not merely accompany stress responses and they actively shape cellular decision-making by controlling signalling cascades, metabolic fluxes, and proteostasis. In this context, PTMs represent mechanistic nodes that translate nano-pesticide induced physicochemical stressors into functional biological outcomes.

Here, we propose that nano-pesticide toxicodynamic cannot be fully understood without explicitly considering PTM-mediated regulatory processes. Nano-particles are known to generate reactive oxygen species, which interfere the membrane integrity, alter metal homeostasis, and impose proteotoxic stress. Each of these stressors converges on PTM networks. For instance, oxidative stress drives reversible thiol modifications and phosphorylation cascade that activate antioxidant defences, while proteotoxic stress engages ubiquitination and acetylation pathways governing protein quality and degradation [57]. Thus, PTM signatures are not only sensitive indicators of nano-pesticide exposure but also direct mediators of microbial adaptation, survival, or dysfunction. Framing PTMs as dynamic regulators rather than passive biomarkers represents a conceptual shift in environmental nano-toxicology.

Current regulatory and risk-assessment frameworks increasingly acknowledge the distinct behaviour of nano-enabled pesticides. Agencies such as the U.S. Environmental Protection Agency (EPA) [101] and international bodies including CSIRO, IUPAC, and APVMA emphasize that nano-pesticide require evaluation criteria beyond those apply to conventional methodologies, particularly with respect to environmental fate, bioaccumulation, and long-term ecological effects [19]. However, existing assessment pipelines remain largely disconnected from molecular regulatory processes, limiting their ability to predict chronic or cumulative impacts on microbiomes. Incorporating PTM-based endpoints could help bridge this gap by providing mechanistically grounded, early-warning indicators of ecological stress. Nano-pesticides encompasses a diverse range of formulations, including polymeric, lipid-based, and inorganic carriers, as well as nanoparticles with inherent pesticidal activity [44].

Metallic nanoparticles, such as zinc oxide (ZnO), silver (Ag), and titanium dioxide (TiO₂) provide antimicrobial and UV-protective functions, while organic nano-carriers carriers enhance stability and bioavailability of encapsulated actives [5, 40]. Although these materials differ in composition and mode of action (Fig. 1), they share a common capacity to interact intimately with microbial cells, making microbiome-level responses a critical but underexplored dimension of nano-pesticide evaluation.

Fig. 1.

The figure illustrates the classification of nano-pesticides based on their composition and mode of action. According to composition, nano-pesticides are broadly categorized into organic and inorganic types. Organic nano-pesticides include polymeric-based formulations such as chitosan derived from fungi, and lipid-based formulations involving solid lipids and pharmaceutical ingredients. In contrast, inorganic nano-pesticides comprise ZnO and TiO₂ nano-based types, which offer UV protection and controlled release, and silver nano-based types, which possess strong antimicrobial properties. Based on mode of action, nano-pesticides are classified into nano-emulsions that improve solubility, dispersion, and uptake in plants and pests; nano-encapsulated pesticides that enable controlled release, prevent degradation, and boost efficiency; nano-carriers for pesticides that reduce runoff and enhance bioavailability; and nanoparticles with inherent pesticidal properties that act by disrupting microbial cell walls or interfering with pest metabolism. Created with BioGDP.com.

Microbiome studies to date have primarily focused on taxonomic shifts detected through next generation sequencing, revealing that nano-pesticides can alter bacterial and fungal communities in soil and aquatic ecosystems [9, 72]. Furthermore, studies have emphasized that while nano-pesticides may reduce the environmental footprint relative to conventional pesticides, they nonetheless pose risks to non-target microorganisms, thereby influencing biodiversity and ecosystem health [48].

While informative, such data provide limited insight into how microbes functionally respond to nano-pesticide exposure or which molecular pathways govern resilience versus vulnerability. Disruption to nutrient cycling, microbial network structure, and ecosystem services may arise from regulatory perturbations long before detectable changes occur in diversity [114]. PTM-centric analyses offer a means to uncover these hidden layers of responses. Integrated PTM aware proteomics with other omics approaches provides a system level strategy to decode nano-pesticide-microbiome interactions. Metagenomics and meta transcriptomics define functional potential and gene expression, while metabolomics captures downstream metabolic consequences. PTM-resolved proteomics uniquely links environmental stress to protein-level regulation, enabling discrimination between adaptive signalling and toxic disruption [67, 71]. The application of nano-pesticides, which may alter microbial communities, has the potential to induce stress responses in microbes, possibly triggering PTMs to regulate survival mechanisms (Fig. 2) [26]. Advances in computational pipelines and PTM-enabled analytical platforms now make such integrative analyses feasible at the community scale [3, 64].

Fig. 2.

The figure illustrates the classification of nano-pesticides based on their composition and mode of action. According to composition, nano-pesticides are broadly categorized into organic and inorganic types. Organic nano-pesticides include polymeric-based formulations such as chitosan derived from fungi, and lipid-based formulations involving solid lipids and pharmaceutical ingredients. In contrast, inorganic nano-pesticides comprise ZnO and TiO₂ nano-based types, which offer UV protection and controlled release, and silver nano-based types, which possess strong antimicrobial properties. Based on mode of action, nano-pesticides are classified into nano-emulsions that improve solubility, dispersion, and uptake in plants and pests; nano-encapsulated pesticides that enable controlled release, prevent degradation, and boost efficiency; nano-carriers for pesticides that reduce runoff and enhance bioavailability; and nanoparticles with inherent pesticidal properties that act by disrupting microbial cell walls or interfering with pest metabolism. Created with BioGDP.com.

In this review, we synthesize current knowledge on nano-pesticide-microbiome interactions through a PTM-cantered framework. We critically evaluate how nano-pesticide-induced stressors reshape microbial regulatory networks, highlight mechanistic links between PTMs and toxicodynamic outcomes, and identify key gaps limiting causal inference Finally, we outline future directions leveraging multi-omics integration and AI-driven analytics to establish PTMs as predictive, mechanistically grounded tools for assessing nano-pesticide safety and guiding the design of environmentally compatible nano-formulations.

Mechanisms of nano-pesticide-microbiome interactions

Nano-pesticides and microbiome interactions present a dual nature, encompassing both the potential for synergistic benefits to plant health and the risk of ecological disruption. Unlike conventional pesticides, characterized by the encapsulation of active ingredients within nanocarriers, nano-pesticides combine chemical activity with nano specific physicochemical properties like high surface area, enhanced reactivity, and controlled release. These features fundamentally alter how active ingredients interact with microbial cells and soil matrices [46, 50]). Understanding these interactions therefore requires moving beyond binary classifications of “toxic” versus “safe” and instead focusing on mechanistic pathways, exposure thresholds, and regulatory responses that determine microbial outcomes. Figure 3 illustrates these interactions and highlights mitigation strategies that emerge from a mechanistic perspective.

Fig. 3.

The figure illustrates how nano-pesticides affect soil microbial communities and highlights mitigation strategies. After application, nano-pesticides enter the soil, reduce microbial diversity, and shift community composition toward pesticide-resistant groups such as Actinobacteria and Rhizobia. At the cellular level, they induce ROS production, membrane damage, DNA impairment, and enzyme inhibition. Proposed mitigation approaches include improving soil health through organic amendments and crop rotation, designing eco-friendly and controlled-release nano-formulations, long-term ecological monitoring, supportive regulatory frameworks, and microbiome engineering to enhance microbial resilience and sustainability in agriculture. Created with BioGDP.com.

Antimicrobial versus growth-promoting outcome

Nano-pesticide demonstrate a compelling duality, functioning as antimicrobial agents under some conditions while promoting plant growth and beneficial microbial activity under others. Antimicrobial effects are primarily attributed to nano induced oxidative stress membranes disruption, and interference with core metabolic processes [65]. At the cellular level, these stressors converge on redox homeostasis, protein stability, and signal transduction pathways, often leading to growth inhibition or cell death in susceptible microorganisms. In contrast, comparable nano-formulations can stimulate plant growth and indirectly support beneficial microbiomes by enhancing nutrient availability, photosynthetic efficiency, and phytohormone signalling [115].

Positive interactions frequently arise from the function of nanoparticles as nutrient nano-carriers. For instance, Zinc and iron oxide nanoparticles, characterized by their high surface area and reactivity, serve as efficient micronutrient carriers, facilitating the targeted delivery of zinc and iron to plant tissues [80]. Improved plant nutrition alters root exudation patterns, which in turn reshapes rhizosphere microbial communities in ways that favour plant growth promoting taxa. This methodology not only addresses micronutrient deficiencies but also fosters sustainable fertilizer utilization and mitigates environmental runoff. Other nanomaterials, including mesoporous silica nanoparticles and carbon-based structures, have been utilized to improve nutrient use efficiency by protecting nutrients from premature degradation and modulating release rates in accordance with plant demand [106]. Conversely, the antimicrobial efficacy of silver and copper oxide is directly associated with their adverse effects on non-target soil microbes [84]. This apparent contradiction reflects not opposing mechanisms but differential regulatory responses within microbial systems. Beneficial outcomes typically arise when nano-pesticide exposure induces adaptive stress signalling, whereas antimicrobial effects dominate when stress exceeds the buffering capacity of microbial regulatory networks.

Dose-dependent dynamics

A defining feature of nano-pesticide-microbiome interaction exhibit a significant concentration-dependent relationship, frequently adhering to a pattern where low doses promote microbial activity while high doses exert inhibitory or toxic effects (Table 1). For example, at low concentrations, zinc oxide nanoparticles enhance the growth and functional activity of plant growth-promoting rhizobacteria such as Bacillus subtilis, a key contributor to rhizosphere health [82, 96]. At optimal concentrations, ZnO nanoparticles improve the colonization capacity of B. subtilis by facilitating biofilm formation on plant roots, thereby providing a protective environment for bacterial communities and facilitating nutrient exchange between plants and microbes [4]. It also enhances the biosynthesis of lipopeptides antibiotics such as surfactin, fengycin, and iturin, strengthening biocontrol activity against phytopathogens [15, 51, 100]).

Table 1.

Dose-dependent effects of common nano-pesticides on soil microbiota

Nano-Pesticide	Dose (mg/kg soil)	Effect on soil Microbiota	Affected Microbial Groups	References
Nano-CuO	0.1–1.1	Limited effects	--	[90]
	100	Significant reductions in denitrification, nitrification, and soil respiration activities	Microbial activities related to C and N cycles	[118].
	500 & 1000	Negative impact on soil microbes, significant decrease in microbial biomass	Decrease in enzymatic activities for urease, phosphatases, and dehydrogenases	[108]
Nano-ZnO	5, 10, and 20	Enhance the relative abundance of the essential bacterial group Bacillus, but higher concentrations show harmful effects on the bacterial population	Bacterial group like Bacillus spp.	[112]
Nano-TiO₂	0.05–500	Non-classical dose-response relationships; nitrification reduced by 25% at lowest (0.05 mg/kg) and highest (100 and 500 mg/kg) concentrations	Ammonia-oxidizing archaea (AOA), bacteria (AOB), Nitrobacter, Nitrospira	[89]
	1–10	Stimulated growth of soil microflora (N-fixers and ammonia oxidizers) increases enzymatic activity	Soil fungi and bacteria	[49]
	20	Decreases the growth of the soil microflora
Nano-Ag	10 & 100	Enhance nitrogen fixation and nitrification, inhibit denitrification via downregulating nirS, nirK, and nosZ genes,	Increased the relative abundances of nitrogen-fixing Microvirga and Bacillus by 0.02% to 629.39% and 14.44% to 30.10%, respectively	[21]
	100	Alter soil bacterial groups associated with the carbon, nitrogen, and phosphorus cycle	Significantly increase the soil pH and alter bacterial groups	[117]
Nano-TiO2	500	Reduced the abundance of ammonia-oxidizing microorganisms in soil, while denitrifying community was not suppressed	Negatively affect the ammonia-oxidizing microorganisms	[91]
Nano-Cu(OH)₂	100	Significant changes in bacterial and fungal community structures; decreased richness and diversity	Bacterial and fungal communities	[72]
Nano-Fe7(PO4)6	50	Significantly increased the relative abundance of beneficial microorganisms associated with nutrient accumulation, which will accelerate nutrient accumulation	--	[43]
Nano-SiO₂	0.5	Promoted antifungal activity of Pseudomonas protegens against Candida albicans	Soil bacteria and fungi	[56]
Nano-SiO2	--	Stimulate the synthesis, transport, and secretion of organic acids in rice roots, which provide a rich carbon source for the rhizosphere microorganisms	Increases the abundance of beneficial microorganisms such as Proteobacteria and Actinobacteria	[113]
Nano-Se	--	Enhances organic acid biosynthesis and transport genes in plants,	Soil microbial community (recruits Sphingomonas and Streptomyces to enhance their interaction with plants and promote growth)	[42]

These functional gains are accompanied by improved induction of plant immune responses, particularly induced systemic resistance, which primes defence-related gene expression [68]. Collectively, these effects reduce reliance on conventional chemical pesticides and promote soil fertility through enhanced microbial function [122]. There exists potential for biofertilizer formulations incorporating ZnO nanoparticles to improve microbial survival and efficacy in field applications [62]. While low doses of ZnO NPs exhibit beneficial effects, it is imperative to regulate their concentrations to mitigate toxicity risks and ensure long-term soil health [76]. This threshold behaviour highlights a critical gap in current risk assessment approaches, which rarely integrate microbial regulatory capacity into toxicity evaluation.

Conversely, exposure to elevated concentrations of copper and silver oxide nanoparticles has been extensively linked to detrimental effects on nitrogen-fixing bacteria such as Azotobacter vinelandii, Rhizobium spp., and Bradyrhizobium japonicum, that are essential for sustaining soil fertility and facilitating plant growth [2]. Exposure to Ag nanoparticles disrupts membrane integrity, generates reactive oxygen species, and directly inactivates nitrogenase through interactions with iron-sulphur clusters and molybdenum cofactors [75]. These effects translate into reduced nitrogen fixation efficiency and compromised nitrogen cycling. Similarly, CuO nanoparticles impair symbiotic nitrogen-fixing bacteria such as Rhizobium and Bradyrhizobium, interfering with membrane function, DNA integrity, and key metabolic enzymes [20, 31]. The ecological consequence is a disruption of plant-microbe mutualisms that cannot be inferred from microbial abundance data alone (Fig. 4).

Fig. 4.

Schematic representation illustrating the effects of nanomaterials on plants, plant-associated microbiomes, and microbially mediated biogeochemical processes. The left panel shows direct plant-nanomaterial interactions, highlighting inhibitory effects such as reduced seed germination, chlorophyll content, and growth (Ag, MWCNTs, ZnO), as well as growth-promoting effects including enhanced photosynthesis, seed germination, and phytohormone induction (TiO₂, MWCNTs, Ag). Upward arrows indicate stimulation, while downward arrows denote inhibition. The right panel depicts nanomaterial-induced changes in microbial populations and functions affecting nutrient cycling and plant growth. Created with BioGDP.com.

To synthesize these context-dependent responses and reconcile contradictory findings across studies, Table 2 summarizes the influence of nanoform properties, soil type, exposure duration, PTM responses, and associated microbial functional outcomes.

Table 2.

Context-dependent relationships between nano-pesticide exposure, PTM responses, and microbial functional outcomes

Study type	Nanoform	Soil type	Exposure duration	PTM reported	Functional microbial outcome	References
Lab microcosm	Ag-NPs (citrate-coated)	Loamy agricultural soil	7 to 14 days	Protein acetylation, oxidation	Increase nitrification and microbial respiration at low doses	[21, 124]
Pot experiment	ZnO-NPs	Organic-rich soil	21 days	Phosphorylation	Increase Bacillus spp. abundance and plant growth promotion	[109]
Pot experiment	ZnO-NPs	Low-carbon soil	30 to 60 days	Acetylation	Decrease nitrifier activity due to Zn²⁺ toxicity	[105]
Lab microcosm	CuO-NPs	Clay soil	14 days	Phosphorylation, ubiquitination	Increase stress-response proteins, altered redox balance	[73]
Field-simulated mesocosm	TiO₂-NPs	Agricultural soil	60 to 90 days	Limited PTM detection	Minimal functional disruption and adaptive microbial response	[116]

These studies collectively demonstrate that PTM responses and microbial functional outcomes are strongly influenced by nanoform characteristics, soil properties, and exposure duration, explaining the apparent contradictions reported across the literature.

The moderating role of soil properties

Nano-pesticide impacts are further shaped by soil physicochemical and biological properties, which act as higher-order modulators of microbial exposure and response. Soil pH strongly influences nanoparticle aggregation, mobility, and dissolution. Acidic conditions often accelerate the release of metal ions from ZnO and CuO nanoparticles, increasing bioavailable toxicity, while in alkaline conditions, increased dispersion and microbial contact [30]. These pH-driven transformations highlight the need to evaluate nano-pesticides as dynamic entities rather than static materials. Soil organic matter plays a pivotal role in mitigating NP toxicity through several mechanisms, including surface modification, complexation and immobilization, and enhanced microbial resilience ([16]). Organic coatings can attenuate nanoparticle-induced stress by limiting ion dissolution and buffering oxidative effects [63]. Furthermore, organic rich soils support robust microbial communities that are better equipped to withstand NP-induced stress, partly due to increased nutrient availability and habitat complexity [27].

Microbial diversity itself constitutes a critical resilience factor. Highly diverse communities exhibit functional redundancy, allowing ecosystem processes to persist even when sensitive taxa are suppressed [34, 86]. Diverse microbial communities are more likely to encompass species or strains with inherent resistance or adapt to nanoparticle exposure over time, thereby preserving ecosystem functions [45]. Certain microorganisms have the capacity to alter the chemical form of NPs through processes such as reduction, oxidation, or methylation, potentially detoxifying NPs or affecting their mobility and bioavailability in the soil [41]. These community-level processes suggest that nano-pesticide outcomes emerge from interactions between material properties, soil context, and microbial regulatory capacity.

Critical synthesis and context dependency of nano-pesticide effects

Although several studies report either stimulatory or inhibitory effects of nano-pesticides on soil microbial processes, these outcomes are highly context dependent rather than universally beneficial or detrimental. Contradictory responses, such as stimulation of nitrification at low nano-Ag concentrations but suppression of nitrogen cycling at higher doses, likely arise from differences in nanoform properties (size, coating, dissolution rate), soil physicochemical characteristics (pH, organic matter, clay content), and exposure duration. For example, ZnO nanoparticles have been shown to promote the growth of Bacillus spp. in organic-rich soils through zinc micronutrient supplementation, while the same particles suppress ammonia-oxidizing bacteria in sandy or low-carbon soils due to increased Zn²⁺ bioavailability and oxidative stress. These findings indicate that nano-pesticide impacts cannot be generalized and must be interpreted within specific soil-nanoform exposure frameworks.

Detection and risk assessment of nano pesticide impacts on microbiomes

The sustainable deployment of nano-pesticides requires risk-assessment frameworks that extend beyond residue quantification to capture biologically meaningful impacts on soil microbiomes, which underpin nutrient cycling, soil structure, and plant health. Conventional chemical assays are well suited for measuring total pesticide loads but are poorly equipped to resolve nano-specific behaviours such as particle transformation, bioavailability, and sub-lethal biological effects. As a result, detection strategies must be coupled with functional and regulatory assessments to meaningfully evaluate ecological risk (Table 3). Importantly, most existing approaches focus on presence and community composition, leaving a critical gap in understanding how nano-pesticides perturb microbial regulatory networks at the molecular level.

Table 3.

Detection technologies for nano-pesticide residues

Detection Method	Principle	Advantages	Limitations	Reference
Electrochemical Sensors	Detect changes in electrical signals due to interactions with nano-pesticides	High sensitivity; rapid response; suitable for on-site detection	Potential interference from complex sample matrices; requires calibration	[7]
Fluorescence Spectroscopy	Measures fluorescence emitted by nano-pesticide interactions	High sensitivity; non-destructive; suitable for real-time monitoring	Fluorescence quenching by sample components; requires specific fluorophores	[107]
Surface-Enhanced Raman Spectroscopy (SERS)	Enhances Raman scattering signals using nanostructured substrates	Ultra-sensitive; capable of detecting low concentrations; rapid analysis	Substrate reproducibility issues; potential interference from background signals	[7]
High-Performance Liquid Chromatography (HPLC)	Separates compounds based on interactions with stationary and mobile phases	High accuracy; widely used standard method	Time-consuming; requires expensive equipment and skilled personnel	[50, 95]
Gas Chromatography-Mass Spectrometry (GC-MS)	Separates and identifies compounds based on mass-to-charge ratio	High sensitivity and specificity; suitable for volatile compounds	Not suitable for non-volatile or thermally labile compounds; requires derivatization	[25]
Colorimetric Sensors	Visual colour change upon interaction with nano-pesticides	Simple; rapid; does not require sophisticated instruments	Lower sensitivity; subjective interpretation of results	[7]
Aptamer-Based Sensors	Uses nucleic acid sequences that bind specifically to nano-pesticides	High specificity and affinity; adaptable to various detection platforms	Stability of aptamers can be affected by environmental conditions	[7]
Microfluidic Devices	Manipulates small volumes of fluids for detection	Requires minimal sample; rapid analysis; potential for integration with portable devices	Fabrication complexity; potential for clogging in channels	[7]
QuEChERS Method	Sample preparation technique for extracting pesticide residues	Quick, easy, cost-effective; suitable for multi-residue analysis	Primarily a sample preparation method; requires subsequent analytical detection methods	[83]
Enzyme-Linked Immunosorbent Assay (ELISA)	Uses antibodies to detect specific nano-pesticides	High specificity; relatively quick and cost-effective	Limited to known compounds; potential for cross-reactivity	[39]

Advanced technologies for nano-pesticide detection

Recent advancements in nano-biosensor technologies have significantly transformed the real-time, on-site monitoring of nano-pesticides within environmental matrices. Contemporary research has refined both microcantilever-based sensors and carbon nanotube (CNT)-based sensors, resulting in enhanced sensitivity, accelerated response times, and improved integration into portable platforms [53, 93]. These developments represent a significant improvement over laboratory-bound analytical techniques, particularly for early contamination detection. Microcantilever sensors have been optimized through surface functionalization strategies that enhance selectivity toward specific nano-pesticide formulations. By incorporating tailored biorecognition elements, these sensors convert binding events into measurable mechanical deflections, achieving detection limits down to parts per trillion in controlled studies [37]. Such sensitivity allows early identification of contamination hotspots and supports real-time field monitoring under dynamic soil conditions [69]. However, while these sensors excel at detection, they provide limited insight into biological consequences, underscoring the need for downstream functional analyses.

Advancement in CNT-based sensors have primarily focused on the integration of aptamer and antibody functionalization, which significantly enhances the specificity for pesticide molecules. Recent studies have demonstrated that the combination of carbon nanotubes with these biorecognition elements enables the detection of nano-pesticide residues at parts-per-billion levels within complex environmental matrices [123]. Innovations in the fabrication of CNT sensors have resulted in the development of flexible, miniaturized sensor arrays that are highly suitable for field deployment [54]. Despite their promise, CNT-based platforms primarily report chemical presence rather than biological impact, highlighting a recurring disconnect between detection and ecological relevance.

Advancements in metagenomic sequencing technologies have significantly enhanced the resolution with which soil microbial communities can be profiled in response to exposure to nano-pesticides. Techniques such as 16 S rRNA gene sequencing for bacterial communities and Internal Transcribed Spacer (ITS) sequencing for fungal communities provide comprehensive insights into microbial diversity and community structure [85]. By comparing microbial profiles from treated and untreated soils, researchers are able to identify shifts in key taxa and detect early signs of microbial stress. For instance, alterations in the abundance of sensitive groups such as Acidobacteria-6 have been associated with changes in soil chemistry and can serve as biomarkers for nano-pesticide exposure [61]. A prominent study utilized nanopore-based sequencing to analyse the impact of silver (Ag), titanium dioxide (TiO₂), and zinc oxide (ZnO) nanoparticles on soil bacterial populations. This methodological approach facilitated the acquisition of full-length 16 S rRNA gene sequences, thereby providing deeper taxonomic insights [8, 38]. The investigation revealed that, while the dominant bacterial phyla, Firmicutes and Proteobacteria, remained largely stable, specific orders within these phyla exhibited significant changes. For example, the abundance of Clostridiales decreased markedly in the presence of Ag nanoparticles, with similar reductions observed for Vibrionales and Rhodobacterales within the Proteobacteria phylum. These findings highlight the sensitivity of certain bacterial subgroups to nanoparticle exposure, thereby underscoring the utility of full-length 16 S rRNA sequencing in detecting such shifts [38]. Although ITS sequencing is a potent tool for profiling fungal communities, specific studies focusing on ITS-based analyses of fungal responses to nanoparticle exposure remain sparse [70]. Nevertheless, the critical role of fungi in soil ecosystems implies that the application of ITS sequencing could yield valuable insights into the influence of nanoparticles on fungal diversity and functionality. High-throughput ITS sequencing has the potential to elucidate alterations in fungal communities subjected to various contaminants, including engineered nanoparticles, thereby contributing to a more comprehensive understanding of soil ecosystem resilience [12].

These metagenomic approaches not only quantify microbial diversity but also detect subtle changes in community composition, serving as early warning indicators of environmental disturbances. By leveraging advanced sequencing technologies, researchers can more effectively assess the ecological risks associated with nanoparticle usage and inform strategies for sustainable environmental management.

Functional impact analysis and its limitations

To bridge the gap between community structure and ecosystem function, predictive tools such as PICRUSt2 have been widely applied to infer metabolic potential from marker gene datasets. By predicting the abundance of genes and metabolic pathways, PICRUSt2 enables researchers to estimate the impact of nano-pesticides on critical processes such as the nitrogen and carbon cycles. Disruptions in these pathways can result in diminished nutrient availability, thereby affecting plant health and soil fertility [23]. Applications in agricultural soils suggest that nano-pesticides often induce subtler functional perturbations than conventional pesticides, potentially reflecting reduced broad-spectrum toxicity [47, 59].

While these predictions are informative, they remain inferential and do not capture post-translational regulation. Functional predictions assume that gene presence equates to activity, an assumption that is increasingly challenged under stress conditions, whereas protein modification, degradation, or inactivation may dominate microbial responses. Consequently, reliance on predictive functional tools alone may underestimate the regulatory impact of nano-pesticides, particularly when stress responses are mediated through rapid PTM-driven signalling rather than changes in gene abundance.

Towards an integrated risk assessment framework

An effective risk assessment framework for nano-pesticide must integrate chemical detection, community profiling, and functional regulation into a unified strategy. Current tolls enable (i) identify contamination hotspots through sensitive biosensors; (ii) detection of microbial community shifts via high-throughput sequencing; and (iii) prediction of metabolic consequences weakly connected to molecular regulatory mechanisms. Incorporating PTM-aware proteomics into this framework offers a critical missing dimension. PTM signatures can reveal early stress responses, distinguish adaptive from maladaptive regulation, and link nano-pesticide exposure directly to toxicodynamic mechanisms such as oxidative stress, proteostasis imbalance, and metabolic reprogramming. When combined with existing detection and functional prediction tools, PTM-centric analyses can transform risk assessment from a descriptive exercise into a mechanistically grounded evaluation of ecological impact.

Advancing such integrated frameworks will be essential for aligning nano-pesticide innovation with long term soil health and ecosystem stability. As regulatory agencies increasingly recognize the limitations of conventional assessment methodologies, multi-layered approaches that include molecular regulatory endpoints are likely to play a central role in guiding the safe and sustainable application of nano-enabled agrochemicals [120].

Limitations and inconsistencies in PTM-based biomarker interpretation

While post-translational modifications offer high sensitivity for detecting early microbial stress responses, PTMs do not always correlate linearly with functional toxicity or ecosystem impairment. Transient PTM changes may reflect adaptive regulation rather than harmful effects, particularly during short-term or low-dose nano-pesticide exposure. In addition, PTM detection is influenced by protein turnover rates, extraction efficiency, and analytical coverage in metaproteomic workflows. Under conditions of chronic exposure or in highly heterogeneous field soils, PTM signals may be masked by community turnover or functional redundancy, limiting their predictive power. These limitations highlight the need to interpret PTM data alongside functional assays and multi-omics integration rather than as standalone indicators.

PTM as molecular bio-signatures of microbial stress responses

Microorganisms inhabit environments characterized by rapid and often unpredictable physicochemical fluctuations. In such contexts, adoptive responses that rely solely on transcriptional reprogramming may be too slow to ensure survival. Post-translational modifications (PTMs) provide a rapid and energetically efficient regulatory layer by directly modulating protein function, stability, localization, and interaction networks. Unlike gene expression changes, PTMs operate on pre-existing proteins, enabling near-immediate cellular responses to environmental stressors, including nano-pesticide exposure. This temporal advantage positions PTMs as sensitive molecular bio-signatures capable of capturing early, sub-lethal stress responses that are frequently overlooked by conventional ecological and toxicological endpoints. Nano-pesticides impose distinct stress regimes on microbial cells, including oxidative imbalance, membrane perturbation, metal ion dysregulation, and proteotoxic stress. These physicochemical insults converge on PTM-regulated signalling and metabolic pathways, making PTM landscapes reflective not only of exposure but also of the underlying toxicodynamic mechanisms. Thus, PTMs should be viewed not merely as correlational biomarkers, but as functional regulators that actively shape microbial resilience, susceptibility, or collapse under nano-pesticide pressure.

Key PTMs mediating microbial adaptation

Protein phosphorylation represents a pivotal PTMs modification that is integral to microbial adaptation in response to stress induced by nano-pesticides. Through the reversible addition of phosphate groups to specific amino acid residues, including serine, threonine, or tyrosine, phosphorylation enables rapid modulation of enzyme activity and signal propagation [14]. In bacterial two component systems exemplify phosphorylation driven environmental sensing, wherein membrane bound sensor kinases identify environmental stressors, such as nanoparticle induced stress, and relay this information to response regulators via phosphor transfer reactions [10]. Exposure to nano-pesticides can activate these signalling circuits indirectly through oxidative stress, membrane damage, or altered ion fluxes. Activated response regulators subsequently reprogram cellular processes such as redox homeostasis, cell envelope remodelling, and stress protein expression. In fungi phosphorylation-driven MAPL and stress-activated protein kinase pathways similarly coordinate responses to nanoparticle exposure by integrating external stress signals with metabolic and transcriptional outputs [13, 121]. Importantly, phosphorylation dynamics often precede detectable transcriptional changes, underscoring their value as early indicators of nano-pesticide-induced stress signalling. Nano-pesticides, particularly metal-based and reactive nanomaterials, are potent inducers of protein misfolding and oxidative damage. Ubiquitination plays a central role in maintaining proteostasis under such conditions by selectively targeting damaged or misfolded proteins for degradation. This PTM involves the covalent attachment of ubiquitin to substrate proteins, marking them for processing by the proteasome or related degradation systems [77]. Under nano-pesticide stress, ubiquitination serves a dual function. First, it prevents the accumulation of dysfunctional proteins that could impair essential cellular processes. Second, it reallocates cellular resources toward the synthesis of protective proteins, including chaperones and detoxification enzymes [104]. Furthermore, ubiquitination plays a pivotal role in modulating the activity of key regulatory proteins that govern stress response pathways, thereby enhancing microbial resilience [125]. This mechanism is particularly salient in bacteria and fungi subjected to nano-pesticide exposure, as the oxidative stress and metal toxicity associated with nanoparticles frequently result in protein damage that necessitates prompt clearance [92]. Lysine acetylation has emerged as a widespread regulatory mechanism linking metabolic state to stress adaptation. Nano-pesticides frequently generate reactive oxygen species, either directly through surface reactivity or indirectly via metal ion release, imposing oxidative stress on microbial cells. Acetylation modulates the activity of key antioxidant enzymes, including superoxide dismutase and catalase, thereby influencing the efficiency of reactive oxygen species detoxification [99]. Beyond antioxidant defence, acetylation extensively targets central metabolic enzymes, reshaping energy production and resource allocation under stress [26]. In bacteria, acetylation and phosphorylation of enzymes in glycolysis, the TCA cycle, and nitrogen metabolism enable rapid metabolic rerouting in response to environmental constraints (Fig. 5; [74]). Well-characterized examples, such as AceK-mediated phosphorylation of isocitrate dehydrogenase and PII-controlled adenylation of glutamine synthetase, illustrate how PTMs exert post-translational control over metabolic flux. These regulatory mechanisms are particularly relevant under nano-pesticide exposure, where metabolic flexibility may determine microbial survival or decline. In fungi, acetylation also extends to chromatin-associated proteins, linking nano-pesticide-induced stress to epigenetic regulation. Histone acetylation can alter chromatin accessibility, enabling the rapid induction of stress-responsive genes without permanent genetic changes [28]. Such epigenetic plasticity may contribute to stress memory and prolonged tolerance following repeated nano-pesticide exposure.

Fig. 5.

Widespread post-translational modification of bacterial metabolic enzymes. Schematic of central carbon metabolism in bacteria showing enzymes subject to phosphorylation (P), acetylation (Ac), uridylation (Ur), glutarylation (Gl), Pupylation (Pu), succinylation (Su) and adenylylation (Ad). Circular and arrow symbols are color-coded to indicate an activating (red) or inhibitory (orange) effect, remaining are unknown function. Created with BioGDP.com.

PTMs in microbial detoxification and nano-pesticide tolerance mechanism

Detoxification of nano-pesticides requires coordinated regulation of metabolic enzymes, transport systems, and stress-response pathways. PTMs are integral to this coordination, enabling fine-scale control over enzyme activity, substrate specificity, and protein turnover. Phosphorylation, for instance, can induce conformational changes that alter catalytic efficiency or substrate affinity of detoxification enzymes, thereby modulating the rate at which toxic compounds are transformed or neutralized.

Specific PTMs, such as phosphorylation and acetylation, play critical roles in regulating enzymes involved in the metabolism and detoxification of nanoparticles. Phosphorylation, defined as the addition of phosphate groups to specific amino acid residues, can alter an enzyme’s conformation and catalytic activity (Fig. 6). Proteomic studies in Pseudomonas aeruginosa have identified numerous phosphorylated proteins associated with central metabolic pathways, stress defences, and transport processes, highlighting phosphorylation as a key regulatory layer in environmental adaptation [79, 103]). Similarly, acetylation, which involves the transfer of acetyl groups to lysine residues, also influences enzyme activity and stability. Although these studies were not exclusively focused on nano-pesticides, the same regulatory architectures are likely engaged under nanoparticle-induced stress, given the overlap in oxidative and metabolic challenges.

Fig. 6.

Nano-pesticides penetrate the microbial cell and trigger primary stresses, including reactive oxygen species (ROS) generation, oxidative damage, and protein misfolding. These stresses activate post-translational modifications (PTMs) such as phosphorylation, ubiquitination, and other regulatory modifications. Phosphorylation cascades (kinase–phosphatase cycle) activate transcription factors, while ubiquitination directs misfolded proteins to proteasomal degradation or chaperone-assisted refolding. Activated transcription factors drive the expression of detoxifying enzymes (CAT, SOD) and protective molecules (GSH, MTS), ultimately restoring cellular homeostasis or, in severe stress, leading to proteome collapse and cell death. This figure centralizes PTMs as key molecular switches in microbial adaptation to nano-pesticide exposure. Created with BioGDP.com.

Efflux pumps represent another critical component of microbial detoxification, actively exporting toxic compounds to reduce intracellular burden. PTMs can regulate efflux pump expression, assembly, and transport efficiency. Phosphorylation-mediated modulation of membrane transport proteins has been reported in multiple bacterial systems, suggesting a conserved mechanism by which cells adjust efflux capacity in response to environmental stress [22, 94, 102]). While specific studies on PTM-mediated regulation of efflux pumps in the context of nano-pesticide tolerance remains limited, this represents a key knowledge gap with strong mechanistic plausibility. Collectively, these findings support a model in which PTMs orchestrate microbial detoxification not through isolated pathways, but via integrated regulatory networks spanning metabolism, proteostasis, and membrane transport. Decoding these PTM-driven networks is therefore essential for understanding how microbial communities respond to nano-pesticides at functional and mechanistic levels [94].

Evidence from real metaproteomic datasets supporting PTM-based biomarkers

To ground the concepts discussed in this review within empirical evidence, we highlight representative studies that have applied metaproteomic profiling to real environmental samples, with particular relevance to stress-induced protein regulation and PTM-associated responses. These examples demonstrate how large-scale protein datasets capture functional activity and microbial adaptation in complex ecosystems, providing a foundation for PTM-based biomarker discovery. Soil metaproteomic has been successfully used to monitor ecological responses in contaminated environments. In a recent study, metaproteomic analysis of multi-contaminated soils undergoing phytoremediation revealed shifts in microbial metabolic activity linked to ecosystem recovery. Proteins involved in central carbon metabolism and molecular chaperones such as GroEL and HSP70 were significantly enriched, and these proteins are known to undergo regulatory post-translational modifications under environmental stress conditions, highlighting their potential as PTM-based stress biomarkers [98]. A comprehensive metaproteomic investigation of a deep-sea hydrothermal vent microbial community identified 2,919 unique proteins and over 1,300 PTM events (including acetylation, methylation, phosphorylation, oxidation, and nitrosylation) and demonstrated that PTMs were enriched within functional groups related to energy metabolism, signal transduction, and ion transport, processes critical for adaptation to extreme environments. This study provides empirical evidence that PTM patterns can reflect microbial responses to environmental stressors and supports their use as functional biomarkers in diverse habitats [119]. Complementary evidence comes from a real soil metaproteomic dataset generated using de novo peptide sequencing from native agricultural soils. The application of a deep learning-based pipeline enabled robust protein identification without matched metagenomes, capturing functional proteins associated with microbial stress tolerance and adaptation [58]. This methodological advance is particularly relevant for PTM analysis in nano-pesticide-treated soils, where incomplete reference databases remain a challenge. Beyond soil systems, metaproteomic studies in engineered environments such as activated sludge exposed to organic pollutants have demonstrated differential expression of degradation-related enzymes, many of which are regulated at the post-translational level [60]. Together, these examples support the view that real metaproteomic datasets provide actionable functional insights and establish a strong empirical basis for using PTMs as sensitive biomarkers of microbial responses to nano-pesticide-induced stress in soil ecosystems. Explicitly linking PTM patterns to environmental stress responses strengthens the connection between theoretical frameworks and biological activity in complex communities.

Most existing studies rely on short-term laboratory microcosms with simplified soil matrices, which may overestimate nano-pesticide bioavailability and stress responses compared with field conditions. Differences between laboratory and field studies, limited exposure durations, and inconsistent nano-pesticide characterization introduce biases that complicate cross-study comparisons. Furthermore, metaproteomic analyses are often constrained by incomplete reference databases, potentially underrepresenting PTMs in low-abundance but functionally critical taxa. Addressing these methodological limitations is essential for translating PTM-based biomarkers into robust tools for environmental risk assessment. Together, these findings demonstrate that both nano-pesticide impacts and PTM responses are highly conditional, reinforcing the need for context-aware interpretation rather than universal biomarker assumptions.

A computational and AI-driven framework for decoding interactions

The complex interactions among nano-pesticides, soil microbiomes, and PTMs require computational frameworks that obvious integrate PTM-centric information across multiple omics layers. Rather than serving as standalone computational analyses, artificial intelligence (AI) and advanced bioinformatics are increasingly being applied to link microbial community shifts with functional protein regulation and stress-responsive PTM signatures, thereby enabling biomarker discovery and mechanistic interpretation [97, 110]. This section presents a computational perspective on key biological inquiries (Table 4).

Table 4.

Workflow and tools for microbiome analysis in nano-pesticide studies

Step	Objective	Method/Tool	Details/Examples
Sample Collection	Collect environmental samples from nano-pesticide-treated and control sites	Sterile soil/water sampling kits	Maintain sterile conditions; record metadata (location, time, pesticide type)
DNA Extraction	Isolate high-quality microbial DNA	Qiagen PowerSoil, DNeasy kits	Optimized for soil/rhizosphere/root environments
DNA Quality Check	Assess DNA purity and concentration	Nanodrop, Qubit, Gel Electrophoresis	Ensures suitability for downstream applications
Library Preparation	Amplify target regions or prepare total DNA libraries	16 S rRNA, PCR for bacteria; Shotgun WMS prep kits	Amplicon or whole metagenome approaches
Sequencing	Generate sequence data	Illumina MiSeq/HiSeq/NovaSeq, Oxford Nanopore	Platform choice depends on resolution and budget
Data Preprocessing	Filter low-quality reads, remove adapters	FastQC, Trimmomatic, Cutadapt	Ensures high-quality input for analysis
Taxonomic Profiling	Identify microbial community composition	QIIME2, DADA2, Kraken2, MetaPhlAn	16 S: QIIME2; WMS: Kraken2 or MetaPhlAn
Functional Annotation	Predict metabolic pathways and resistance genes	HUMAnN3, KEGG, EggNOG, PROKKA	Insight into functional shifts due to nano-pesticides
PTM Analysis	Identify post-translational modifications in proteins	MaxQuant, PEAKS, Proteome Discoverer	Mass spectrometry-based proteomics for PTM detection
Statistical Analysis	Compare groups and detect significant changes	R (phyloseq, vegan), Python (scikit-bio, pandas)	ANOVA, PERMANOVA, diversity indices
AI/ML Integration	Predict microbial shifts and responses	Random Forest, SVM, Neural Networks (TensorFlow, Scikit-learn)	Train models using microbiome + metadata for prediction
Network Analysis	Identify microbial interactions	Cytoscape, CoNet, SparCC	Network-based inference of microbial ecology
Visualization	Represent data graphically	R (ggplot2, plotly), Python (matplotlib, seaborn)	PCA, heatmaps, bar plots, NMDS

At the community level, 16 S rRNA gene sequencing and whole-metagenome shotgun (WMS) sequencing provide essential context for interpreting PTM dynamics by identifying taxa and functional genes responsive to nano-pesticide exposure [117]. have shown that silver nanoparticles (AgNPs), copper oxide (CuO), and zinc oxide (ZnO) nanoparticles alter soil microbial composition and enrich genes associated with oxidative stress, metal resistance, and membrane transport [36, 66, 81]. AI-assisted clustering and feature selection approaches, including Random Forests and dimensionality-reduction algorithms, are increasingly used to associate these genomic shifts with downstream proteomic and PTM responses, enabling the identification of stress-responsive microbial signatures. Functional interpretation moves beyond gene presence through the integration of metagenomics with metaproteomic and PTM analysis. AI-enhanced metaproteomic pipelines, including MaxQuant and MetaProteomeAnalyzer, support high-confidence peptide identification and site-specific PTM annotation using curated resources such as UniMod and PTM-Shepherd ([17, 18, 32]). Recent environmental metaproteomic studies have demonstrated that PTMs such as phosphorylation and acetylation are enriched in proteins involved in signal transduction, transport, and stress regulation, highlighting their role in microbial adaptation to nanoparticle-induced stress. These PTM patterns provide functional readouts that cannot be inferred from genomic data alone.

AI-driven pattern recognition further enhances PTM biomarker discovery by identifying modification signatures that correlate with specific nano-pesticide exposures. Machine-learning models trained on MS/MS spectra enable the classification of PTM profiles associated with oxidative stress, efflux regulation, and metabolic rewiring, offering predictive insight into microbial resilience or sensitivity. Such approaches are particularly valuable in soil systems, where incomplete reference databases and high community complexity limit conventional analyses.

Finally, PTM-aware computational frameworks support evolutionary interpretation by linking short-term regulatory PTM responses with longer-term genomic adaptation. Integration of metagenome-assembled genomes (MAGs) with PTM-resolved proteomics allows strain-level resolution of resistance mechanisms and regulatory plasticity, while AI-assisted pangenome and mobile genetic element analyses help contextualize PTMs within adaptive trajectories [11, 87]. Overall, condensing computational analyses around PTM-aware multi-omics integration transforms AI from a descriptive tool into a mechanistic framework (Fig. 7). Such approaches are central to establishing PTMs as sensitive, functionally meaningful biomarkers of soil microbial responses to nano-pesticides, aligning computational advances directly with the biological focus of this review.

Fig. 7.

AI-driven workflow for PTM-based biomarker discovery in nano-pesticide–exposed soil microbiomes: The schematic illustrates a conceptual framework linking nano-pesticide exposure to post-translational modification (PTM)-based biomarker identification. Soil samples from nano-pesticide-treated and control conditions undergo parallel microbiome profiling (16 S rRNA gene sequencing and whole metagenome sequencing) and metaproteomic analysis to capture expressed proteins and PTMs. Artificial intelligence and machine learning approaches integrate taxonomic, functional, and PTM datasets through feature extraction, pattern recognition, and network analysis. This integrated framework enables the identification of stress-responsive PTM signatures, microbial taxa, and functional pathways that serve as sensitive biomarkers of nano-pesticide-induced stress and support predictive ecological risk assessment. Created with BioGDP.com.

Mitigation strategies and emerging challenges

The growing utilization of nano-pesticides in agriculture underscores the urgent need for the formulation of effective mitigation strategies to address potential environmental and health concerns. Prominent approaches encompass bioremediation techniques and the implementation of sustainable alternatives to traditional nano-pesticides. Bioremediation utilizes microbial processes to degrade or detoxify environmental contaminants, including nano-pesticides. Post-translational modifications (PTMs) of microbial enzymes can enhance their degradative capabilities. For instance, ligninolytic enzymes, such as laccases and peroxidases, have been employed in waste management and bioremediation due to their efficacy in degrading complex organic pollutants. However, challenges related to enzyme stability under varying environmental conditions remain [29]. The application of microbial consortia, which consist of communities of interacting microorganisms, has demonstrated promise in the detoxification of persistent pesticides and the enhancement of soil recovery. These consortia are capable of degrading complex compounds more efficiently than individual strains, thereby providing a robust solution for bioremediation initiatives [126].

To diminish reliance on synthetic nano-pesticides, the development of bio-based nano-formulations has garnered significant attention. These formulations employ natural products, such as plant extracts, encapsulated within nanoparticles to enhance their efficacy and stability. For example, a recent study proposed a nano-biopesticide formulation comprising green synthesized silver nanoparticles derived from Ocimum sanctum leaf extract, demonstrating potential as an environmentally friendly alternative for pest management [33]. Engineered microbial biocontrol agents represent another sustainable strategy. By leveraging beneficial microorganisms to combat pests and diseases, these agents provide an environmentally benign approach to crop protection. Advances in nanotechnology have facilitated the creation of nano-biopesticides based on microbial biocontrol agents, thereby offering targeted and efficient solutions for pest management [52].

Proactive and regulatory oriented approach

In the domain of nanotechnology-enabled pesticides, several critical challenges persist that warrant attention. Firstly, there are significant knowledge gaps concerning the long-term ecotoxicological effects. Specifically, research regarding the long-term ecotoxicology and transgenerational impacts on host microbiomes remains limited, which may have implications for soil health and non-target organisms [24]. Addressing these deficiencies is imperative for the design of next-generation nano-pesticides that are both effective and sustainable.

A second prominent challenge is the precise formulation of these products. Innovative “smart” nano-carriers, engineered to exhibit pH and enzyme responsiveness, present promising avenues for achieving site-specific delivery. These advanced formulations can release active ingredients solely when triggered by specific environmental conditions, such as the acidic environment found in an insect gut, thereby minimizing off-target effects and enhancing the safety profile [88].

Advancing field of nano-pesticides introduces significant regulatory and risk assessment challenges that necessitate a global, harmonized approach. The urgency for global guidelines on nano-pesticides is increasingly apparent, as the rapid integration of nanotechnology into agricultural practices surpasses the capabilities of existing regulatory frameworks. The development of standardized, internationally recognized protocols would facilitate the assurance of safety, efficacy, and environmental protection across various regions. Concurrently, there is a pressing demand for the standardization of post-translational modification (PTM)-based biomarkers for environmental monitoring. These biomarkers, which emphasize post-translational modifications, have the potential to provide sensitive and specific measures of environmental exposures and impacts [60]. Establishing uniform methodologies for their application would enhance the reliability of environmental assessments and support informed decision-making in both regulatory and research contexts. Recent literature highlights these challenges and underscores the necessity for integrated, globally consistent regulatory guidelines to effectively manage the complexities associated with nano-pesticide applications and environmental monitoring. Ultimately, overcoming these obstacles will facilitate the development of more precise, safer, and environmentally benign nano-pesticide applications in contemporary agriculture.

Conclusion

This review demonstrates that post-translational modifications (PTMs) are feasible and promising biomarkers for assessing soil microbial responses to nano-pesticide exposure. Unlike conventional endpoints that rely on bulk toxicity or community composition shifts, PTMs capture rapid, protein-level regulatory responses that reflect functional stress, adaptation, and resilience at sub-lethal exposure levels. Their high sensitivity, functional relevance, and close linkage to microbial metabolic and signalling pathways provide distinct advantages for early warning and mechanism-based risk assessment. However, several challenges must be addressed before PTMs can be routinely integrated into regulatory frameworks, including methodological standardization, limited reference databases for environmental microbes, complexity of PTM interpretation in mixed communities, and the need for cross-ecosystem validation. Overcoming these constraints will be critical for translating PTM-based insights into robust tools for nano-pesticide risk assessment and environmental monitoring. Looking forward, there is a pressing need to standardize PTM-based methodologies and validate them across diverse environmental contexts to ensure comparability across studies and regulatory reliability. It is imperative to advocate for long-term ecological and global policy frameworks that integrate PTM-informed risk assessments to guide the responsible deployment of nano-agrochemicals. Lastly, integrative bioinformatics approaches present powerful tools for modelling evolutionary trajectories, the emergence of resistance, and systems-level shifts in microbial communities, establishing a foundation for safer, more intelligent, and sustainable nano-enabled agriculture.

Author contributions

Atul Kumar Srivastava and Xin Xie conceived and designed the study. Pooja Mishra and Simpal Kumari performed writing and making diagram. Nisar Uddin, Songshu Chen and Yudan Zhao assisted with review and comments to manuscript. Xin Xie supervised the manuscript and acquired funding.

Funding

This research was funded by the National Natural Science Foundation of China (32272514), and the Guizhou Provincial Science and Technology Project ([2022]091).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not Applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.