Impact of Different Treatment Methods and Timings on Soil Microbial Communities with Transgenic Maize Straw Return

Impact of Different Treatment Methods and Timings on Soil Microbial Communities with Transgenic Maize Straw Return | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of Different Treatment Methods and Timings on Soil Microbial Communities with Transgenic Maize Straw Return Yanbo Xie, Jun-Yan Xiang, Likun Long, Yue Ma, Zhenjuan Xing, Ling Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5612017/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 6 You are reading this latest preprint version Abstract Understanding the impact of genetically modified (GM) crop straw return on soil ecosystems is crucial as GM crops become more prevalent. This study assesses the effects of straw mulching and deep plowing on soil microbial communities from GM and non-GM maize, highlighting potential ecological impacts. High-throughput metagenomic sequencing was utilized to analyzed the microbial community structure and functional genes in soil samples collected at different times (30, 180, and 270 days) after straw mulching and deep plowing treatments. The study included insect-resistant transgenic maize varieties 2A-7 and CM8101 and their non-transgenic counterparts B73 and Zheng58. Different treatment methods significantly affect soil microbial alpha-diversity and beta-diversity, with deep plowing resulting in higher alpha-diversity compared to mulching, and the 180-day mark exhibiting the highest alpha-diversity across all sampling times. Early straw treatment prompted a rapid microbial response to nutrient availability, with notable changes in diversity and function over time. Straw treatments notably altered soil microbial functions, especially in carbon cycling and nutrient metabolism. Interestingly, the microbial effects of GM versus non-GM maize straw were similar, suggesting crop residue type under consistent soil management practices might not significantly alter microbial community structures. The methods and timing of straw treatments have a significant impact on soil microbial communities, surpassing the differences between GM and non-GM straw. These findings highlight the importance of straw management practices for sustainable agricultural ecosystem management. Biological sciences/Microbiology/Environmental microbiology Biological sciences/Genetics Biological sciences/Plant sciences Soil Microbiology Genetically Modified Crops Straw Decomposition Metagenomic Sequencing Agricultural Sustainability Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Soil, being a critical component of terrestrial ecosystems on Earth, plays a pivotal role in crop growth, ecological equilibrium, and environmental quality, as its health directly influences these factors 1 . As a crucial component of the soil ecosystem, soil microorganisms play a key role in nutrient cycling and plant health, owing to their diversity and functional capabilities 2 . In recent years, with the advancement and application of genetic engineering technologies, genetically modified (GM) crops have been widely cultivated due to their advantages in increasing yield and resistance to pests and diseases 3 . However, the potential impacts of incorporating the residual biomass of GM crops into soil on the structure and function of soil microbial communities remain unclear. The treatment of GM crop residues may exert an important impact on soil microbial communities. Mulching or deep plowing are prevalent methods for incorporating crop residues, which can alter soil structure and enhance soil organic matter, potentially impacting the composition and functionality of soil microbial communities 4 , 5 . Although existing studies indicate that GM crops may influence soil microbial communities, most research focuses on the period during crop growth 6 . There are relatively fewer studies on the long-term effects of residues after returning to the field, especially their effects on soil microbial community and function. Transgenic insect-resistant maize CM8101, developed by the Institute of Crop Science, Chinese Academy of Agricultural Sciences, using Zheng58 as the genetic background, has shown significant insecticidal effects against the Asian corn borer 7 . Studies indicate that CM8101 does not significantly affect the main physicochemical properties and enzyme activities of rhizosphere soil 8 , nor does it impact the springtails, earthworms 9 , or the rhizosphere microbial community and its functions 10 . Similarly, transgenic insect-resistant maize 2A-7, based on the B73 maize genetic background with the introduction of mcry1Ab and mcry2Ab genes, enables the host plant to produce Bacillus thuringiensis (Bt) toxins, thereby effectively resisting various pest attacks. Research demonstrates that this transgenic maize has no significant impact on rhizosphere microorganisms 11 . Transgenic maize 2A-7 and CM8101 have obtained safety certificates in China and have shown promising application prospects. This study, based on these two transgenic maize varieties, aims to explore the impact of insect-resistant transgenic crop straw mulching and deep plowing on the composition and function of soil microbial communities. Utilizing high-throughput metagenomic sequencing technology, a comparative analysis was conducted on the community structure, diversity, and functional genes in soil microbiota before and after different treatments. This study provides a deeper understanding of the impacts of GM crop straw and residues on soil microbial communities, which is crucial for assessing the sustainability of GM crops within agricultural ecosystems. This research supports the environmental impact assessments for the large-scale industrialization of GM crops, offering important scientific data with significant prospective and practical relevance. Additionally, these findings will contribute to more comprehensive scientific bases for environmental risk assessments and management strategies for GM crops. Results Soil and Straw Component Analysis Table 1 Total nitrogen, phosphorus, potassium, and moisture content in soil and differently treated straw samples Sample Total Nitrogen (%) Total Phosphorus (%) Total Potassium (%) Moisture (%) Soil 0.142 ± 0.003 0.066 ± 0.001 2.146 ± 0.035 4.198 ± 1.115 CM8101_CK 0.713 ± 0.029 0.068 ± 0.005 1.150 ± 0.022 — CM8101_T 0.785 ± 0.024 0.078 ± 0.005 1.135 ± 0.061 — 2A-7_CK 0.600 ± 0.010 0.070 ± 0.000 0.633 ± 0.017 — 2A-7_T 0.340 ± 0.066 0.040 ± 0.000 1.068 ± 0.051 — This study measured the total nitrogen, phosphorus, and potassium content of soil and powdered straw samples treated differently, and also determined the moisture content of the soil samples (Table 1 ). Non-transgenic Zheng 58 and B73 maize straw samples showed differences in total potassium content, with Zheng 58 having higher levels. In comparisons between transgenic and non-transgenic straws, transgenic Zheng 58 maize straw carrying the CM8101 gene exhibited higher total nitrogen content than its non-transgenic counterpart, suggesting an enhanced capacity for nitrogen absorption or utilization. Transgenic B73 maize straw with the 2A-7 gene showed lower nitrogen content but higher potassium content, which may indicate changes in the metabolism or absorption mechanisms for nitrogen and potassium. These results provide baseline data for subsequent analyses of the impact of straw return on soil microbial communities. Microbial Alpha Diversity Analysis This study assessed the impact of transgenic straw treatment on the Alpha diversity of soil microbial communities, including species richness index, Shannon diversity index, and Simpson diversity index (Fig. 1 ). The results indicated that soil samples treated with straw exhibited significant differences in Alpha diversity compared to the untreated control (p-values were not indicated in the figure). However, comparisons of soil microbiota treated with GM straw to those treated with non-GM straw revealed no significant statistical differences in species richness, Shannon index, or Simpson index (with p-values ranging from 0.1 to 1.0), indicating that GM does not affect the Alpha diversity of soil microbial communities (Fig. 1 A and B). As time progressed, the Alpha diversity of the soil microbial communities exhibited significant variations (Fig. 1 C). Notably, in nearly all time-point comparisons, significant statistical variations were observed in alpha-diversity indexes, with alpha-diversity reaching its peak at the M6 time point. However, a significant decrease in alpha-diversity was noted at the M9 time point. This pattern may reflect the dynamic changes in soil alpha-diversity following straw decomposition. Specifically, the initial phase of straw decomposition might have provided additional nutrients, fostering an increase in microbial diversity, which led to the observed peak in alpha-diversity at the M6 time point. However, over time, these nutrients might have been progressively depleted by the microbial community, leading to a decline in microbial diversity at the M9 time point. These observations underscore the significant impact of sampling time on the alpha-diversity of soil microbial communities. In assessing the impact of straw treatment methods on soil microbial communities' alpha diversity, comparisons between mulching and deep plowing revealed notable differences. The Shannon diversity index showed a p-value of 0.04 and the Simpson index had a p-value of 0.08, indicating that straw treatment methods substantially influence soil microbial Alpha. In summary, straw treatment notably affected the Alpha diversity of soil microbial communities, yet no significant differences were observed statistically between GM and non-GM straw treatments. Concurrently, sampling time had a significant impact on the Alpha diversity of soil microbial communities, while the specific methods of straw treatment (mulching versus deep plowing) also exhibited notable influence. Microbial Beta Diversity Analysis PCoA was utilized to assess beta diversity, revealing the impact of various factors on the distribution patterns of beta-diversity (Fig. 2 ). In the temporal dimension, the distinct separation between samples indicates that the composition of soil microbiota underwent significant changes over time, reflecting that time is a key factor affecting beta-diversity. The changes in the distribution of samples at different time points reflect the impact of straw decomposition in soil on the composition of microbial communities. In contrast, the treatment methods of mulching and deep plowing showed no significant differences in the microbial community structure on PCo1 and PCo2 axes, indicating that these treatment methods, while different in approach, result in similar impacts on microbial beta-diversity. Regarding genotypes, the overlap of samples indicates that the straw treatments from different genotypes do not significantly alter the beta-diversity of the soil microbial community. Similarly, no notable distinction in beta-diversity was observed between samples on the level of GM versus non-GM, suggesting that the transgenic traits of the straw do not decisively alter the overall structure of the microbial community. This could be because the microbial communities are not particularly sensitive to the transgenic traits of the straw, or the transgenic traits do not significantly change the overall chemical composition of the straw, thereby exerting a limited impact on the microbial community. Overall, these results accentuate the significant effect of temporal changes related to straw decomposition on soil microbial beta-diversity and also suggest the potential influences of treatment methods, genotypes, and GM status on the beta-diversity of soil microbial communities. Differences in Microbial Community Composition The phylum-level stacked bar chart represents the relative abundance of various microbial phylum across different samples (Fig. 3 ). One month into the experiment (at sampling point M1), considerable differences in the composition of microbial phyla were observed among groups based on different genotypes and treatment methods. This indicates that GM straw and soil management strategies have important initial impacts on soil microbial diversity. Specifically, deep plowing treatment in Zheng 58 significantly decreased the abundance of Planctomycetes, whereas the same treatment in B73 markedly decreased the abundance of Acidobacteria, Chloroflexi, Thaumarchaeota, and Gemmatimonadetes, and increased the abundance of Bacteroidetes and Proteobacteria, indicating the distinct impacts of maize straw from different cultivars on the soil microbial community structure. As time progressed to six months (sampling point M6), the microbial composition of the various treatment groups began to converge, suggesting that the soil microbial community composition is moving towards a new equilibrium after the initial disturbance. This convergence may indicate a certain resilience within the soil microbial communities, which are capable of adapting to environmental changes brought about by the cultivation of GM straw. By the nine-month mark of the experiment (sampling point M9), a reduction in the relative abundance of Proteobacteria was observed across all GM straw sample groups compared to non-GM groups, with proportions more closely aligning with initial conditions. These results reflect the impact of long-term straw return on soil ecological changes, which may be associated with factors such as the type of crops, GM traits, farming practices, and sampling time points. The composite volcano plot (Fig. 4 ) illustrates the significant variations in microbial community composition at the genus level due to different treatment methods and genetic modification. Additionally, a supplementary table is provided to display variations at the species level (Supplementary Material 1). Compared to mulching, deep-tillage samples exhibited significant abundance changes across numerous genera. In all non-transgenic samples, mulching significantly enriched Zancudomyces ( Zancudomyces culisetae ), while deep tillage led to an enrichment of Acremonium ( Acremonium chrysogenum ), Stachybotrys ( Stachybotrys chartarum , Stachybotrys chlorohalonata ), Geosmithia ( Geosmithia morbida ), Tolypocladium ( Tolypocladium ophioglossoides , Tolypocladium paradoxum ), Metarhizium ( Metarhizium robertsii ), Trichoderma ( Trichoderma harzianum , Trichoderma gamsii ), Cordyceps , and Pochonia , among others. In transgenic samples, the genera enriched in deep tillage were similar to those found in non-transgenic deep-tilled samples, although differences may exist at the species level (such as Metarhizium anisopliae ). For the non-transgenic 2A-7 B73 maize straw soil samples, mulching treatment resulted in an enrichment of Hapalosiphon , Gloeocapsopsis , and Ardenticatena , while deep tillage led to an enrichment of genera such as Bacteriovorax , Acremonium ( Acremonium chrysogenum ), Stachybotrys ( Stachybotrys chartarum ), Metarhizium ( Metarhizium robertsii ), Streptomyces ( Streptomyces fulvorobeus ), and Xenorhabdus . In the 2A-7 transgenic B73 maize straw soil samples, mulching treatment enriched genera including Candidatus Nitrosocaldus , Candidatus Nitrosocosmicus , and Nitrososphaera , whereas deep tillage exhibited a similar enrichment of genera to that found in non-transgenic samples with some differences at the species level (such as Stachybotrys chlorohalonata , Metarhizium anisopliae ). In the non-transgenic CM8101 Zheng58 maize straw soil samples, the mulching treatment enriched genera such as Cyanobacterium , Desulfurobacterium , Candidatus Fonsibacter ( Candidatus Fonsibacter lacus ), Candidatus Thalassarchaeum , and Achromatium , whereas deep tillage led to an enrichment of Eutypa ( Eutypa lata ), Hirsutella , Chrysoporthe , Arthrinium , and Ophiocordyceps . In the CM8101 transgenic Zheng58 maize straw soil samples, the mulching treatment resulted in the enrichment of genera including Actinomyces , Desulfobacter , Carboxydothermus , Aphanizomenon , Arcobacter , Elizabethkingia , and Oligella , while deep tillage enriched Tankvirus , Lysobacter , and Jasminevirus among others. Comparisons between non-transgenic and transgenic samples revealed significant enrichment of Microbulbifer , Mucilaginibacter , Cellvibrio , Gilvimarinus , Thalassocella , and Agarilytica in non-transgenic samples, with no significant enrichment observed in the transgenic counterparts. In the non-transgenic versus transgenic comparison of 2A-7 maize, non-transgenic samples were notably enriched with Phyllobacterium , Hydrocarboniclastica , and Alishewanella , while no significant enrichment of any genus was detected in transgenic samples. Lastly, in the comparison between non-transgenic and transgenic CM8101 maize, non-transgenic samples showed significant enrichment of Desulfurispira , Microbulbifer , Endogone , Phycomyces , and Erythromicrobium , whereas transgenic samples were enriched with Candidatus Nitrosocosmicus , Candidatus Nitrosocaldus , and Nitrososphaera . Overall, both transgenic and non-transgenic samples demonstrated similar shifts in microbial communities under the same treatment conditions. Moreover, the number of microbial changes induced by different management practices exceeded those caused by genetic modification. These findings suggest that management practices have a more significant impact on microbial diversity than genetic engineering. Figure 5 illustrates the changes in microbial genera over time in GM and non-GM samples compared to the blank control (BLANK). Variations at the species level are detailed in Supplementary Material 2. At time point M1, non-GM samples exhibited a significant decrease in genera such as Candidatus Nitrosocosmicus , Candidatus Nitrosocaldus , and Thelephora ( Thelephora ganbajun ), while genera such as Metarhizium ( Metarhizium anisopliae ), Geosmithia , Purpureocillium ( Purpureocillium lilacinum ), Roseovarius , and Arthrospira were significantly enriched. In GM samples, genera like Choiromyces and Thelephora ( Thelephora ganbajun ) were reduced, with an enrichment of Arcobacter , Tolypocladium ( Tolypocladium ophioglossoides ), Trichoderma , Pochonia , Sulfurovum , Arthrospira , Flavobacterium akiainvivens , Agrobacterium tumefaciens , etc. At time point M6, non-GM samples showed a notable decrease in genera like Candidatus Nitrosocosmicus and Thelephora ( Thelephora ganbajun ), with an increase in Pelagicoccus , Candidatus Rhabdochlamydia , Saccharophagus , Cellvibrio , Thalassolituus , and Azorhizobium . GM samples demonstrated a reduction in Pleurotus and Thelephora ( Thelephora ganbajun ), with an augmentation of Saccharophagus , Fertoebacter , Cellvibrio , Agarilytica , Taibaiella , and Roseovarius . By time point M9, both non-GM and GM samples revealed a marked decline in Pleurotus and Thelephora ( Thelephora ganbajun ). Non-GM samples were characterized by an enrichment of Saccharophagus , Agarilytica , Gilvimarinus , Teredinibacter , Thalassocella , and Cellvibrio , whereas GM samples were abundant in Saccharophagus , Teredinibacter , Agarilytica , Cellvibrio , Azorhizobium , and Desulfatiglans . Overall, the microbial diversity changes were most abundant at the M6 sampling point. By the M9 sampling point, this diversity had diminished, potentially correlating with the straw decomposition process. Based on the number of significantly changed genera, it can be inferred that straw decomposition might proceed more rapidly in GM straw, or that the soil microbial community shifts in GM straw soils are less pronounced compared to non-GM straw soils relative to blank soil. Based on the characteristic microbial communities identified by LEfSe analysis, a heatmap was constructed using the average proportions of these microbes in each group of samples. To enhance clarity of the inter-group variations, the data were normalized. The heatmap (see Supplementary Material 3) reveals that at the M1 timepoint, samples from deep tilling treatments of both transgenic and non-transgenic Zheng58 maize were markedly enriched in the Actinobacteria class. Cover treated samples of transgenic CM8101 maize collected at M1 and M6 timepoints showed a significant enrichment of microbes belonging to the classes Cytophagia, Anaerolineae, and Gemmatimonadetes. At the M6 timepoint, the 2A-7 transgenic samples were distinctly enriched in Deltaproteobacteria. Additionally, Gammaproteobacteria and Betaproteobacteria were predominantly distinguished in samples from the M1 timepoint. Overall, samples from the M1 timepoint demonstrated certain degrees of distinction in microbial composition compared to those from M6 and M9 timepoints, which may be attributed to the differential impacts of treatment methods on soil microbial community structure associated with the timing of sampling. Predictive Analysis of Microbial Functionality Upon analyzing the impact of straw treatment on soil microbial communities, significant differences in the presence of KEGG metabolic pathways among treatment groups were observed (Fig. 6 ). Compared to the untreated soil, straw treatment predominantly influenced pathways associated with carbon fixation and cycling, such as carbon fixation pathways in prokaryotes, metabolism of 2-oxocarboxylic acids, and metabolism of acetic acid and dicarboxylic acids, as well as pathways related to protein synthesis and degradation, including RNA polymerase, ribosome, and protein export. At the M1 time point, the samples demonstrated significant presence in energy and nutrient metabolism, such as metabolism of fructose and mannose, galactose metabolism, and pyruvate metabolism, indicating that straw provides a rich carbon source for soil microbes. Additionally, the cycling of sulfur and nitrogen was significantly affected, including sulfur metabolism and amino acid metabolism, showcasing the microbial transformation and utilization of nitrogen and sulfur sources during straw decomposition. The presence of functions in the synthesis and breakdown of secondary metabolites (e.g., tryptophan metabolism, porphyrin, and chlorophyll metabolism), as well as nucleic acid and energy metabolism (e.g., purine metabolism, pyrimidine metabolism, one carbon pool by folate), reflects the rapid microbial response to straw addition, involving key biological processes such as biosynthesis, energy metabolism, and environmental adaptation. As time progressed to the M6 and M9 sampling points, the differences in metabolic pathway presence among the samples began to diminish. At the M6 time point, samples showed an enrichment in pathways related to amino acid biosynthesis, fatty acid biosynthesis, and starch and sucrose metabolism, suggesting that the microbial community may have reached a new dynamic equilibrium. By the M9 time point, samples exhibited enrichment in pathways such as butyrate metabolism, fatty acid degradation, nitrogen metabolism, and pyruvate metabolism, reflecting the microbial community's adaptation to long-term straw decomposition. Furthermore, differences in KEGG functional presence were observed between the mulching and deep plowing treatment samples. Although the deep plowing treatment resulted in a significantly richer microbial community and higher alpha diversity compared to the mulching treatment, the KEGG functional diversity was richer in the mulching samples. This discrepancy may be attributed to the distinct microenvironments provided by the mulching and deep plowing treatments. While deep plowing enhances microbial species richness by thoroughly mixing straw with the soil, mulching may lead to a specialization in metabolic functions, as reflected in the enrichment of KEGG functions. Overall, straw treatment significantly affected the presence of KEGG metabolic pathways in soil microbial communities, revealing the facilitative role of straw decomposition on soil microbial functional diversity and ecosystem stability. It also demonstrated the varying impacts of different treatment methods and time on the functional potential of soil microbial communities. The results of the eggNOG Pathway analysis (Fig. 7 ) indicated that at the M1 sampling time point, a high level of potential functionality was noted in multiple metabolic pathways, particularly nucleotide transport and metabolism (F), inorganic ion transport and metabolism (P), and carbohydrate transport and metabolism (G). This heightened presence is likely a response to the increased availability of substrates following the addition of straw. By the M6 time point, while the differential presence of some pathways diminished, an increase in the presence of functions related to cell wall, membrane, and envelope biogenesis (M), as well as cell motility (N), suggested a possible trend toward stabilization of the soil microbial community over time. However, at the M9 time point, enhanced presence of functions was again observed in pathways such as amino acid transport and metabolism (E), lipid transport and metabolism (I), secondary metabolites biosynthesis, transport, and catabolism (Q), and signal transduction mechanisms (T), reflecting the microbial community's adaptation to the prolonged decomposition of straw. In transgenic samples, a particularly pronounced presence of functions was evident in pathways involved in defense mechanisms (V), amino acid transport and metabolism (E), lipid transport and metabolism (I), and secondary metabolites biosynthesis, transport, and catabolism (Q). Collectively, these data reveal the specific impacts of straw management practices on soil microbial functional diversity in both transgenic and non-transgenic crops. Additionally, the findings illustrate the dynamic changes in microbial community functional potential across different time points. Discussion In this study, focusing on transgenic maize 2A-7 and its non-transgenic counterpart B73, as well as transgenic maize CM8101 and its non-transgenic counterpart Zheng58, two treatment methods were applied: straw mulching and deep plowing. The aim was to investigate the specific impact of transgenic straw return to the field on the ecology and function of soil microbiota. The results indicate that the impact of transgenic straw on the diversity and community structure of soil microbiota is limited compared to the effects of time progression and treatment methods. Microbial diversity peaked at the M6 time point, while microbial function exhibited the richest variation at the M1 time point. Deep plowing promoted an increase in biodiversity, whereas mulching led to a concentration of microbial functions. These findings emphasize the decisive role of treatment methods and time in shaping soil microbial ecosystems, highlighting that the direct impact of transgenic traits on soil microbiota is relatively limited. Previous studies have shown that during straw return processes, different crop straws exhibit unique patterns of microbial diversity and activity at various time points. For instance, research on rice straw return found the highest alpha diversity of microbes at 137 days, with microbial activity being more vigorous in the early phase (0–20 days) and slowing down in the later phase (104–137 days) 12 . Similarly, an oat straw return study observed that microbial diversity was higher in the sixth month than in the first month, although microbial activity measured by soil respiration rate was higher initially and then decreased 13 . This study revealed that microbial diversity at the M6 time point (180 days) was significantly higher than at the M1 time point (30 days), and microbial functional activity peaked at M1 before showing a declining trend. This could be associated with the rapid response and nutrient release during the initial phase of straw decomposition, which enhances specific microbial activities. As time progresses and the easily decomposable components of straw are gradually consumed, the decomposition process shifts towards more complex organic matter, necessitating a more diverse microbial involvement. By the 180-day mark, a considerable portion of the straw had been decomposed, with other studies indicating a decomposition rate of about 50% 14 , leading to a relative decrease in available nutrients. However, by the 270-day mark (M9 time point), despite further decomposition, the reduction in available nutrients may have led to a decrease in microbial diversity, reflecting the dynamic changes and adjustments within the microbial community during the straw decomposition process. Specifically, across all time points when compared to control soil, the reduction of Thelephora ganbajun might indicate an increase in competitive pressure during straw decomposition, leading to the loss of survival conditions for this species in the new environment. In non-transgenic samples, the absence of Candidatus_Nitrosocosmicus and Candidatus_Nitrosocaldus suggests alterations in the ammonia oxidation process and nitrogen cycle pathways 15 , 16 . The enrichment of known biocontrol fungi, Metarhizium 17 and Purpureocillium 18 , in non-transgenic M1 samples could imply that the straw itself may offer certain resistance to pests. Meanwhile, in transgenic M1 samples, the enrichment of biocontrol fungi such as Trichoderma 19 and Pochonia 20 further underscores the positive role of transgenic straw return in enhancing the potential for biological control in soil. At the M6 and M9 time points, the consistency of many enriched genera, such as the increase of Saccharophagus 21 and Cellvibrio 22 , indicates that straw return has strengthened the soil's cellulose decomposition capability, facilitating nutrient cycling and the degradation of complex organic matter in straw; the increase in Agarilytica 23 might reflect the activation of specific organic matter decomposition pathways in the soil. During the initial stages of straw return to the field, the observed high levels of energy and nutrient metabolism activity, particularly in nucleotide transport and metabolism, inorganic ion transport and metabolism, and carbohydrate transport and metabolism, may be a direct response of the microbial community to the abundant nutritional resources provided by the added straw. As time progresses, towards the M6 and M9 time points, the microbial community adapts to the new environment, displaying diverse metabolic activities such as enhanced cellular structure and energy metabolism. This reflects the community's evolution towards a stable state and its adaptation to the decomposition of more complex organic materials. These changes reveal the dynamic impact of straw return on the functional diversity of soil microbial communities, highlighting the significant role of time in the process of straw decomposition. In discussing the impacts of deep plowing and mulching treatments, it is noteworthy to consider the significant influence of deep plowing on the soil microbial communities. Regardless of whether in non-transgenic or transgenic deep plowing treatment samples, a variety of fungi and bacteria such as Acremonium , Stachybotrys , Metarhizium , and Trichoderma were enriched. These fungi are commonly associated with the decomposition of organic matter and nutrient cycling 24 , 25 , indicating that deep plowing treatment facilitated the activity of microorganisms closely related to straw decomposition. Particularly , Trichoderma and Metarhizium , which are widely used in biological control 17 , 19 , their enrichment may suggest that deep plowing treatment helps enhance the potential for natural disease management in the soil. Overall, these results demonstrate that deep plowing treatment increased the microbial diversity related to straw decomposition in the soil, increasing the relative abundance of some beneficial microorganisms. This may positively impact soil fertility and plant health. However, further studies are necessary to directly link these microbial changes with specific soil health indicators and plant performance. This study investigated the impacts of straw return from GM and non-GM maize through two different management practices, mulching and deep incorporation, on soil microbial community composition and function. These results indicate that, compared to GM traits, the method and timing of treatment played a more significant role in shaping the composition and function of soil microbial communities. Particularly, deep incorporation significantly enhanced microbial diversity and enriched microorganisms closely associated with straw decomposition, potentially benefiting soil fertility and plant health. Additionally, this research revealed dynamic changes in soil microbial communities in response to straw return, adapting to different stages of straw decomposition. Notably, the impact of GM straw on soil microbes was relatively limited, an important finding for assessing the sustainable application of GM crops in agricultural ecosystems. It suggests that returning GM crop straw is unlikely to adversely affect soil microbial ecology, supporting the integration of GM crops into sustainable agricultural practices without compromising soil microbial diversity and function. In summary, this study underscores the importance of considering management practices and timing in optimizing agricultural practices, while also providing scientific evidence for further research into the role of GM crops within agricultural ecosystems. Methods Materials and Location Four types of maize straw were utilized in this study: (1) non-transgenic B73 maize straw, (2) B73 maize straw GM with the 2A-7 gene, (3) non-transgenic Zheng 58 maize straw, and (4) Zheng 58 maize straw GM with the CM8101 gene. Designate the non-transgenic maize straw as CK. The experimental site was located at the Transgenic Plant Field Testing Base of Jilin Academy of Agricultural Sciences, situated in Gongzhuling City, Jilin Province, which is a proprietary testing ground of the Jilin Academy of Agricultural Sciences. Grouping This experiment was conducted within planting areas of no less than 150 square meters, managed using conventional farming practices and without the use of insecticides throughout the entire growth period. The experiment was replicated six times with the following treatments: No Straw Added Treatment: Soil samples adjusted to the appropriate moisture level were placed in beakers without the addition of maize straw, labeled as 'BLANK'. Mulching Treatment: Soil samples adjusted to the appropriate moisture level were placed in beakers, and then a uniform layer of maize straw was spread on the top, labeled as 'M'. Deep Plowing Treatment: Soil samples adjusted to the appropriate moisture level were thoroughly mixed with maize straw, labeled as 'D'. The experimental sampling times were set as follows: 'M1' denotes sampling conducted 1 month (30 days) after the experiment commenced; 'M6' represents sampling conducted 6 months (180 days) after the start of the experiment; 'M9' indicates sampling conducted 9 months (270 days) post-experiment initiation. Following the naming conventions, which denote the timing of sampling (M1, M6, M9), gene type (2A_7, CM8101), treatment method (M for mulching, D for deep plowing), and sample status (CK for non-transgenic, T for transgenic), this study comprised 25 distinct groups: BLANK, M1_2A_7_M_CK, M1_2A_7_M_T, M1_2A_7_D_CK, M1_2A_7_D_T, M1_CM8101_M_CK, M1_CM8101_M_T, M1_CM8101_D_CK, M1_CM8101_D_T, M6_2A_7_M_CK, M6_2A_7_M_T, M6_2A_7_D_CK, M6_2A_7_D_T, M6_CM8101_M_CK, M6_CM8101_M_T, M6_CM8101_D_CK, M6_CM8101_D_T, M9_2A_7_M_T, M9_2A_7_M_CK, M9_2A_7_D_T, M9_2A_7_D_CK, M9_CM8101_M_CK, M9_CM8101_M_T, M9_CM8101_D_CK, M9_CM8101_D_T. Soil and Straw Sample Component Analysis Soil moisture content was determined using the drying method according to the People's Republic of China national standard (NYT52-1987). The procedure involved weighing approximately 10 grams of soil to determine its wet weight, then drying the sample in an oven at 105°C until constant weight was achieved. After cooling in a desiccator to room temperature, the dry weight was measured. Moisture content was calculated using the formula: Moisture percentage = [(Wet weight - Dry weight) / Wet weight] × 100%. Total potassium and phosphorus contents in the soil were determined based on the forestry industry standards LY_T 1234–2015 and LY_T 1232–2015, respectively, using the alkali fusion method. The procedure included mixing 0.25 g of soil with 2 g of sodium hydroxide, then heating in a high-temperature furnace to 750°C and holding for 15 minutes. After cooling, the sample was treated with water and sulfuric acid to adjust the pH of the solution to an appropriate level before filtration. The filtrate was then directly measured using a photometer for potassium or phosphorus content. Total nitrogen content was determined according to the forestry industry standard LYT1228-2015 using the Kjeldahl method. The soil sample was digested with concentrated sulfuric acid to convert all nitrogen to ammonium form, which was then alkalized and distilled. The ammonia distilled was absorbed by boric acid solution and quantified by titration with a standard acid solution. For samples containing nitrate and nitrite nitrogen, pre-treatment included oxidation with potassium permanganate and reduction with iron powder. Straw samples, prepared as dry powders, were analyzed according to the agricultural industry standard NY525-2012. Total nitrogen was determined using the Kjeldahl method, total phosphorus by the molybdenum blue spectrophotometric method, and total potassium by the flame photometric method. In the molybdenum blue method, samples were digested with a mixture of concentrated nitric and perchloric acids, reacting with ammonium molybdate and ascorbic acid under acidic conditions to form a blue complex, the absorbance of which was measured at 880 nm. For potassium, the digested samples were diluted appropriately and the potassium content was measured at specific wavelengths using a flame photometer. Treatment and Sample Collection In this experiment, 500 mL beakers were filled with 120 g of soil, which had never been planted with GM maize. All soil was finely ground, sieved, air-dried, and weighed. The amount of straw was calculated based on the typical straw return amount in black soil regions (12,000 kg·hm-2) and the average soil bulk density of the plow layer (1.1 g·cm-3), initially resulting in a soil-to-straw ratio of 281:1. However, as adding only 0.4 g of straw to each 120 g of soil was insufficient for covering or mixing, following recommendations, the ratio was adjusted to 5.6:1. Some studies have used a straw addition ratio of 10%. The corresponding maize straw was dried, crushed, thoroughly mixed, and the remaining powder was stored frozen. Before the experiment began, the air-dried soil samples were analyzed for organic carbon content, various nutrient contents, and pH values, and the sieved soil samples underwent microbial sequencing. Each beaker, containing 120 g of air-dried soil covered with a breathable plastic film, was adjusted to field capacity moisture content (about 20%) with distilled water, and it was determined through gradient experiments to periodically spray each beaker with 20 mL of water to maintain moisture. Dynamic sampling points were set for 30, 90, 180, and 270 days, with straw being removed from the soil prior to sampling. Samples were quickly placed in ice boxes and then transferred to a -80°C freezer awaiting sequencing analysis. Microbiome Data Acquisition Metagenomic sequencing and preliminary data processing were conducted at Shanghai OE Biotech Co., Ltd. Approximately 0.5 g of fresh soil sample (stored at -80°C) was used for microbial genomic DNA extraction using the MagPure Soil DNA KF Kit, according to the manufacturer's protocol. After DNA extraction, DNA quality and quantity were assessed by agarose gel electrophoresis and a NanoDrop spectrophotometer. Sequencing libraries were prepared following the standard procedures provided by the sequencing facility, without targeted PCR amplification steps. Trimmomatic 26 was utilized for quality control of the sequencing data to ensure data integrity. Then, the assembly of metagenomic sequences was carried out using MEGAHIT 27 , which is based on the De Bruijn graph principle, resulting in the construction of Contigs. Only Contigs exceeding 500 bp in length were selected for subsequent analysis. Upon the assembly of genomes, Prodigal 28 was applied to predict Open Reading Frames (ORFs) from the Contigs, and the predicted ORFs were then translated into amino acid sequences. To acquire a non-redundant initial Unigene set, the ORF prediction results from all samples were subjected to redundancy removal using CD-HIT 29 , with an identity threshold of 95% and a coverage threshold of 90%, resulting in a non-redundant gene set. Representative sequences from each cluster were selected as Unigenes. For functional annotation, the non-redundant gene sequences (amino acid sequences) were aligned using DIAMOND (v0.9.7) against databases including NR, KEGG 30 , COG 31 , SWISSPROT 32 , and GO 33 . BLAST alignment was performed with an expectation value (e-value) threshold of 1e-10, selecting the best-hit results for functional annotation. Finally, Bowtie2 34 was employed to align the clean reads from each sample with the non-redundant gene set (with 95% identity), thereby obtaining gene abundance information in each sample. This sets the foundation for subsequent bioinformatics analysis and functional annotation. Statistical Analysis and Visualization Data analysis and visualization were conducted in the R programming environment, version 4.3.1. Alpha diversity indices, including species richness, Shannon index, and Simpson index, were calculated based on species-level abundance data using the vegan package 35 . Principal Coordinates Analysis (PCoA) was performed based on Bray–Curtis distances calculated using the vegdist function from the vegan package, and ordination was conducted with the pco function from the labdsv package 36 . The EnhancedVolcano package 37 was employed for volcano plot generation, while the pheatmap package 38 was used for heatmap creation. P-values were calculated using the Wilcox test. Unless otherwise stated, p-values less than 0.05 were considered statistically significant. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval and consent to participate Not applicable Consent for publication Not applicable Funding This work was supported by the Jilin Scientific and Technological Development Program, China (Grant number: 20230508091RC). All sources of funding for all authors relevant to the content of this manuscript have been disclosed. Author Contribution Yanbo Xie: investigation, experiments, writing—original draft preparation; Jun-Yan Xiang: data analysis, visualization, writing—original draft preparation; Likun Long: validation, writing—review and editing; Yue Ma, Zhenjuan Xing, Ling Wang, and Chunyu Shao: experiments; Na Liu: conceptualization, supervision; Feiwu Li: conceptualization, supervision, project administration, funding acquisition. Acknowledgements Not applicable. Data Availability The data supporting the findings of this study are provided within the manuscript and its supplementary materials, as well as the NCBI Sequence Read Archive (SRA) repository, under accession number PRJNA1203889 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1203889/), and are available on request from the corresponding author (Feiwu Li, [email protected] ). References Ellert, B. H., Clapperton, M. J. & Anderson, D. W. Chapter 5 An ecosystem perspective of soil quality. Developments in Soil Science 25 (1997). Yadav, A. N. et al. Biodiversity, and biotechnological contribution of beneficial soil microbiomes for nutrient cycling, plant growth improvement and nutrient uptake. Biocatalysis and Agricultural Biotechnology 33 , 102009 (2021). Kumar, K. et al. Genetically modified crops: current status and future prospects. Planta 251 , 91 (2020). Ji, B. et al. Effects of Deep Tillage and Straw Returning on Soil Microorganism and Enzyme Activities. The Scientific World Journal 2014 , 1-12 (2014). Du, C., Li, L. & Effah, Z. Effects of straw mulching and reduced tillage on crop production and environment: A review. Water 14 , 2471 (2022). Bruinsma, M., Kowalchuk, G. A. & Veen, J. A. V. Effects of genetically modified plants on microbial communities and processes in soil. Biology & Fertility of Soils 37 , 329-337 (2003). Zhang, S. et al. Efficacy evaluation of transgenic cry1ab-ma maize cm8101 for resistance to the ostrinia furnacalis. Journal of Maize Sciences 28 , 59-64. In Chinese (2020). Liang, J., Luan, Y., Song, X. & Zhang, Z. Effects of transgenic insect-resistant maize cm8101 on main physicochemical properties and functional enzyme activities of rhizosphere soil. Zhejiang Agricultural Science 60 , 2248-2252. In Chinese (2019). Lu, X. et al. Effects of transgenic bt maize cm8101 with cry1ab-ma gene on folsomia candida and eisenia fetida. Journal of Environmental Entomology 40 , 390-397. In Chinese (2018). Zhang, X. et al. Effect of gm maize cm8101 on functional diversity of rhizosphere microorganisms. Journal of Northeast Agricultural Sciences 48 , 48-51.In Chinese (2023). Wen, Z. et al. Differential assembly of root-associated bacterial and fungal communities of a dual transgenic insect-resistant maize line at different host niches and different growth stages. Frontiers in Microbiology 13 (2022). Liu, L. et al. Regulation of straw decomposition and its effect on soil function by the amount of returned straw in a cool zone rice crop system. Scientific reports 13 (2023). Kimeklis, A. K. et al. The Succession of the Cellulolytic Microbial Community from the Soil during Oat Straw Decomposition. International journal of molecular sciences 24 , 6342 (2023). Jin, M. et al. High Level of Iron Inhibited Maize Straw Decomposition by Suppressing Microbial Communities and Enzyme Activities. Agronomy 12 , 1286 (2022). Abby, S. S. et al. Candidatus Nitrosocaldus cavascurensis, an Ammonia Oxidizing, Extremely Thermophilic Archaeon with a Highly Mobile Genome. Frontiers in Microbiology 9 (2018). Alves, R. J. E. et al. Ammonia Oxidation by the Arctic Terrestrial Thaumarchaeote Candidatus Nitrosocosmicus arcticus Is Stimulated by Increasing Temperatures. Frontiers in Microbiology 10 (2019). St. Leger, R. J. & Wang, J. B. Metarhizium: jack of all trades, master of many. Open Biology 10 (2020). Dutta, T. K., Khan, M. & Tanaka, K. Purpureocillium lilacinum for plant growth promotion and biocontrol against root-knot nematodes infecting eggplant. PloS one 18 , e0283550 (2023). Yao, X. et al. Trichoderma and its role in biological control of plant fungal and nematode disease. Frontiers in Microbiology 14 (2023). Manzanilla-Lopez, R. H., Esteves, I. & Finetti-Sialer, M. M. Pochonia chlamydosporia Advances and Challenges to Improve Its Performance as a Biological Control Agent of Sedentary Endo-parasitic Nematodes. J Nematol 45 , 1-7 (2013). Zhang, H. & Hutcheson, S. W. Complex Expression of the Cellulolytic Transcriptome of Saccharophagus degradans. Appl Environ Microb 77 , 5591-5596 (2011). Breuil, C. & D.J.Kushner. Cellulase induction and the use of cellulose as a preferred growth substrate by Cellvibrio gilvus. Can J Microbiol 22 , 1776-1781 (1976). Ling, S.-K., Xia, J., Liu, Y., Chen, G.-J. & Du, Z.-J. Agarilytica rhodophyticola gen. nov., sp. nov., isolated from Gracilaria blodgettii. Int J Syst Evol Micr 67 , 3778-3783 (2017). Perdomo, H. et al. Spectrum of Clinically Relevant Acremonium Species in the United States. J Clin Microbiol 49 , 243-256 (2011). Ibrahim, S. R. M. et al. Stachybotrys chartarum—A Hidden Treasure: Secondary Metabolites, Bioactivities, and Biotechnological Relevance. Journal of Fungi 8 , 504 (2022). Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30 , 2114-2120 (2014). Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31 , 1674-1676 (2015). Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11 (2010). Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22 , 1658-1659 (2006). Kanehisa, M., Furumichi, M., Sato, Y., Matsuura, Y. & Ishiguro-Watanabe, M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res 53 , D672-d677 (2025). Tatusov, R. L., Galperin, M. Y., Natale, D. A. & Koonin, E. V. The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res 28 , 33-36 (2000). Bairoch, A. & Apweiler, R. The SWISS-PROT protein sequence data bank and its supplement TrEMBL in 1999. Nucleic Acids Res 27 , 49-54 (1999). Harris, M. A. et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res 32 , D258-261 (2004). Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nature Methods 9 , 357-359 (2012). Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R. & Legendre, P. Vegan:Community Ecology Package. https://CRAN.R-project.org/package=vegan (2022). Roberts, D. W. Labdsv: Ordination and Multivariate Analysis for Ecology. (2023). Blighe, K. et al. EnhancedVolcano: Publication-Ready Volcano Plots with Enhanced Colouring and Labeling. https://doi.org/10.18129/B9.bioc.EnhancedVolcano (2023). Kolde,R.Pheatmap:PrettyHeatmaps.https://CRAN.R-project.org/package=pheatmap (2019). Additional Declarations No competing interests reported. Supplementary Files Supplementalmaterial1.xlsx Supplementalmaterial2.xlsx Supplementalmaterial3.pdf Cite Share Download PDF Status: Published Journal Publication published 10 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 May, 2025 Reviews received at journal 30 Apr, 2025 Reviewers agreed at journal 07 Apr, 2025 Reviewers invited by journal 07 Apr, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 19 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5612017","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440536171,"identity":"a4e7db14-c4d8-4620-b946-66f55fcf177f","order_by":0,"name":"Yanbo Xie","email":"","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yanbo","middleName":"","lastName":"Xie","suffix":""},{"id":440536172,"identity":"5c4c062b-8ae2-472c-b522-0631d18dfc79","order_by":1,"name":"Jun-Yan Xiang","email":"","orcid":"","institution":"University of Leeds","correspondingAuthor":false,"prefix":"","firstName":"Jun-Yan","middleName":"","lastName":"Xiang","suffix":""},{"id":440536173,"identity":"b47ab991-36d8-4d76-9b17-d97b68845f1f","order_by":2,"name":"Likun Long","email":"","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Likun","middleName":"","lastName":"Long","suffix":""},{"id":440536174,"identity":"8f529e3a-4ec0-4706-98d8-29397304a721","order_by":3,"name":"Yue Ma","email":"","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Ma","suffix":""},{"id":440536175,"identity":"c6140d4f-2ff5-45e3-800e-027078e143d5","order_by":4,"name":"Zhenjuan Xing","email":"","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Zhenjuan","middleName":"","lastName":"Xing","suffix":""},{"id":440536176,"identity":"7cd9d971-83ed-4772-b268-e9662619c4cd","order_by":5,"name":"Ling Wang","email":"","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Wang","suffix":""},{"id":440536177,"identity":"149c26d1-6a95-498a-b40e-089a81c316d4","order_by":6,"name":"Chunyu Shao","email":"","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Chunyu","middleName":"","lastName":"Shao","suffix":""},{"id":440536178,"identity":"bf846fc9-5750-455e-8d78-b8b94c08c850","order_by":7,"name":"Na Liu","email":"","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Liu","suffix":""},{"id":440536179,"identity":"2f15a9f5-4e00-4ad2-9d6c-3048b5919111","order_by":8,"name":"Feiwu Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIie3RsQrCMBCA4QuBZgntWnDwFeLiIuqrGApxEdTRLVNdBFcfw8m5cCgOPkChDorQ3UUyiNh2Lm3cHPLP95FLAuBy/WE+JTeYAIy6TMsbCAviUSpKEvU2yUPYEaDVGNGpzEOrxTxWbHaPkZJdpFZmce0Cw+O+ZTEqZIw+47nKuMh7miuVNpPgFMpDccp6pjIQSHTI+y2EMlMQopP5a2kEjm2IBxU5TxRwgdKG0FB+puUjR52CRHHbXYIAydNcBtVXPs0bh1uGp0ZSc+5v4y6Xy+Wq6wtp0kgWfvy0WgAAAABJRU5ErkJggg==","orcid":"","institution":"Jilin Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Feiwu","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-12-10 00:23:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5612017/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5612017/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-09851-w","type":"published","date":"2025-07-10T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80274678,"identity":"9cbba427-3787-45b8-a2f9-3e1118a80b26","added_by":"auto","created_at":"2025-04-10 04:39:00","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1072030,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative analysis of soil microbial alpha diversity across different genotypes, time points, and treatment methods. \u003c/strong\u003eAlpha diversity indices are presented across different groups: (A) individual samples, (B) genotypes, (C) sampling times, and (D) treatment types. Labels: 'BLANK' - soil samples without straw; 'M' - mulching with straw surface layer; 'D' - deep plowing with straw mixed in. Time points: 'M1', 'M6', 'M9' - months post-treatment. Genotypes: '2A_7', 'CM8101'; 'CK' - non-transgenic, 'T' - transgenic. P-values indicate statistical comparisons.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/2fb252c9601a39a83f5fa401.jpg"},{"id":80274680,"identity":"9ec3e9bf-827d-4a86-b803-a76f430bf201","added_by":"auto","created_at":"2025-04-10 04:39:00","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":566224,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrincipal coordinates analysis of soil microbial communities differentiated by genotypes, time points, and treatment methods. \u003c/strong\u003eThe PCoA plot (top left) illustrates the variation in microbial communities at three different sampling times (M1, M6, M9) across treatment types (BLANK, mulching, deep plowing) and genotypes (2A_7 GM, 2A_7 Non-GM, CM8101 GM, CM8101 Non-GM). The box plots (right and bottom) show differences in community composition metrics by time, treatment, and genotype, with p-values indicating statistically significant differences. Symbols represent individual samples plotted according to their treatment and genetic modification status. Labels: 'BLANK' - soil samples without straw; 'M' - mulching with straw surface layer; 'D' - deep plowing with straw mixed in. Time points: 'M1', 'M6', 'M9' - months post-treatment. Genotypes: '2A_7', 'CM8101'; 'CK' - non-transgenic, 'T' - transgenic. P-values indicate statistical comparisons.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/683db2c4e992dd3968f41c7d.jpg"},{"id":80275056,"identity":"fb6596b5-6a77-4b36-b00d-9d5e70262b91","added_by":"auto","created_at":"2025-04-10 04:47:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":442688,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial community composition at the phylum level and phylum abundance across different treatment groups. \u003c/strong\u003e(A) Relative abundance of major bacterial phyla in soil samples.\u003cstrong\u003e \u003c/strong\u003e(B) Boxplots displaying the abundance of select bacterial phyla across different groups. * for p \u0026lt; 0.05 and ** for p \u0026lt; 0.01. Labels: 'BLANK' - soil samples without straw; 'M' - mulching with straw surface layer; 'D' - deep plowing with straw mixed in. Time points: 'M1', 'M6', 'M9' - months post-treatment. Genotypes: '2A_7', 'CM8101'; 'CK' - non-transgenic, 'T' - transgenic. P-values indicate statistical comparisons.\u003c/p\u003e","description":"","filename":"Figure3.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/b1188cfc549d5db7e53c2be5.jpg"},{"id":80275063,"identity":"9352dff1-4777-4954-a422-b8036bcceb9c","added_by":"auto","created_at":"2025-04-10 04:47:00","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":736840,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificant microbial community changes in response to treatment and genetic modification. \u003c/strong\u003eThe composite volcano plot illustrates changes in microbial community composition at the genus level across different treatment methods (deep plowing vs. mulching, represented in top and bottom rows of the M/D panels) and genetic modifications (GM vs. non-GM, shown in top and bottom rows of the G/N panels). Each data point marks a genus, with significant changes highlighted in red (P value \u0026lt; 0.05) and non-significant changes in blue (P value ≥ 0.05). Key microbial genera are annotated to denote increased or decreased abundance due to treatments.\u003c/p\u003e","description":"","filename":"Figure4.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/92479928fa70ce54b3f31cbb.jpg"},{"id":80274684,"identity":"0801436a-7017-4817-93da-2d6015cb47d3","added_by":"auto","created_at":"2025-04-10 04:39:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":741515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignificant microbial community changes in response to time. \u003c/strong\u003eThis plot visualizes microbial genus shifts over three time points (1, 6, and 9 months), comparing Non-GM and GM samples across these periods. Each dot represents a genus, with significant abundance changes shown in red (P value \u0026lt; 0.05) and non-significant in blue (P value ≥ 0.05). The arrangement from left to right highlights temporal variations, with a clear distinction between GM and Non-GM conditions. The 'BLANK' section at the bottom of each panel serves as a control reference.\u003c/p\u003e","description":"","filename":"Figure5.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/3d3b7908221ecca1a8cd5092.jpg"},{"id":80274695,"identity":"53fc39b9-097e-4e8e-9dc2-96fc2fc730a2","added_by":"auto","created_at":"2025-04-10 04:39:00","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":920304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG pathway analysis across different maize straw soil treatments. \u003c/strong\u003eKEGG pathway enrichment analysis showing significantly enriched microbial metabolic pathways identified by LEfSe analysis in each sample group. Circle size represents Linear Discriminant Analysis (LDA) scores, reflecting the significance of pathway enrichment. Color gradient indicates the logarithmic scale (Logarithm Value) of pathway abundance. Sample labels indicate sampling times (M1: 1 month, M6: 6 months, M9: 9 months), maize genotypes (2A_7, CM8101), treatment methods (M: mulching, D: deep plowing), and genetic modification status (CK: non-transgenic, T: transgenic). \"BLANK\" represents untreated soil without straw addition.\u003c/p\u003e","description":"","filename":"Figure6.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/e6d6114eb8594113f1ab19e3.jpg"},{"id":80275654,"identity":"68b5a66b-1a13-42aa-8466-3bb21ea095a4","added_by":"auto","created_at":"2025-04-10 04:55:00","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":405029,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEggNOG pathway analysis across different maize straw soil treatments. \u003c/strong\u003eeggNOG functional category enrichment analysis showing significantly enriched functional categories identified by LEfSe analysis in each sample group. Circle size represents Linear Discriminant Analysis (LDA) scores, reflecting the degree of enrichment significance. The color gradient indicates the logarithmic scale (Logarithm Value) of the relative abundance of each functional category. Sample labels specify sampling times (M1: 1 month, M6: 6 months, M9: 9 months), maize genotypes (2A_7, CM8101), treatment methods (M: mulching, D: deep plowing), and genetic modification status (CK: non-transgenic, T: transgenic). \"BLANK\" indicates untreated soil without straw addition.\u003c/p\u003e","description":"","filename":"Figure7.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/ef645831b9d91d0833758bbd.jpg"},{"id":86699276,"identity":"8849ce8c-7e63-4683-b791-5ad096014219","added_by":"auto","created_at":"2025-07-14 16:07:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6008033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/2daf1eac-00aa-418c-b310-41799ab9e70b.pdf"},{"id":80275059,"identity":"75ce236a-47b9-409c-89b2-7001595063f9","added_by":"auto","created_at":"2025-04-10 04:47:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":240121,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/352ee5ac045abaf931ad93ba.xlsx"},{"id":80275655,"identity":"999a37d7-79b0-4a17-969f-28b13cd79767","added_by":"auto","created_at":"2025-04-10 04:55:00","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":170174,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/701f1617a2cc7048534e180e.xlsx"},{"id":80274686,"identity":"fed65ef9-7c37-4a14-a0d9-a6144ed87c40","added_by":"auto","created_at":"2025-04-10 04:39:00","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":442780,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterial3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5612017/v1/e54f5f3d0a1dbba6deefe167.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Different Treatment Methods and Timings on Soil Microbial Communities with Transgenic Maize Straw Return","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil, being a critical component of terrestrial ecosystems on Earth, plays a pivotal role in crop growth, ecological equilibrium, and environmental quality, as its health directly influences these factors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As a crucial component of the soil ecosystem, soil microorganisms play a key role in nutrient cycling and plant health, owing to their diversity and functional capabilities \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In recent years, with the advancement and application of genetic engineering technologies, genetically modified (GM) crops have been widely cultivated due to their advantages in increasing yield and resistance to pests and diseases \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, the potential impacts of incorporating the residual biomass of GM crops into soil on the structure and function of soil microbial communities remain unclear.\u003c/p\u003e \u003cp\u003eThe treatment of GM crop residues may exert an important impact on soil microbial communities. Mulching or deep plowing are prevalent methods for incorporating crop residues, which can alter soil structure and enhance soil organic matter, potentially impacting the composition and functionality of soil microbial communities \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Although existing studies indicate that GM crops may influence soil microbial communities, most research focuses on the period during crop growth \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. There are relatively fewer studies on the long-term effects of residues after returning to the field, especially their effects on soil microbial community and function.\u003c/p\u003e \u003cp\u003eTransgenic insect-resistant maize CM8101, developed by the Institute of Crop Science, Chinese Academy of Agricultural Sciences, using Zheng58 as the genetic background, has shown significant insecticidal effects against the Asian corn borer \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Studies indicate that CM8101 does not significantly affect the main physicochemical properties and enzyme activities of rhizosphere soil \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, nor does it impact the springtails, earthworms \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, or the rhizosphere microbial community and its functions \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Similarly, transgenic insect-resistant maize 2A-7, based on the B73 maize genetic background with the introduction of \u003cem\u003emcry1Ab\u003c/em\u003e and \u003cem\u003emcry2Ab\u003c/em\u003e genes, enables the host plant to produce \u003cem\u003eBacillus thuringiensis\u003c/em\u003e (Bt) toxins, thereby effectively resisting various pest attacks. Research demonstrates that this transgenic maize has no significant impact on rhizosphere microorganisms \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTransgenic maize 2A-7 and CM8101 have obtained safety certificates in China and have shown promising application prospects. This study, based on these two transgenic maize varieties, aims to explore the impact of insect-resistant transgenic crop straw mulching and deep plowing on the composition and function of soil microbial communities. Utilizing high-throughput metagenomic sequencing technology, a comparative analysis was conducted on the community structure, diversity, and functional genes in soil microbiota before and after different treatments. This study provides a deeper understanding of the impacts of GM crop straw and residues on soil microbial communities, which is crucial for assessing the sustainability of GM crops within agricultural ecosystems. This research supports the environmental impact assessments for the large-scale industrialization of GM crops, offering important scientific data with significant prospective and practical relevance. Additionally, these findings will contribute to more comprehensive scientific bases for environmental risk assessments and management strategies for GM crops.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSoil and Straw Component Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal nitrogen, phosphorus, potassium, and moisture content in soil and differently treated straw samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Nitrogen (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal Phosphorus (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Potassium (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMoisture (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.142\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.066\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.146\u0026thinsp;\u0026plusmn;\u0026thinsp;0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.198\u0026thinsp;\u0026plusmn;\u0026thinsp;1.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCM8101_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.713\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.150\u0026thinsp;\u0026plusmn;\u0026thinsp;0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCM8101_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.785\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.078\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.135\u0026thinsp;\u0026plusmn;\u0026thinsp;0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2A-7_CK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.600\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.070\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.633\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2A-7_T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.340\u0026thinsp;\u0026plusmn;\u0026thinsp;0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.040\u0026thinsp;\u0026plusmn;\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis study measured the total nitrogen, phosphorus, and potassium content of soil and powdered straw samples treated differently, and also determined the moisture content of the soil samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Non-transgenic Zheng 58 and B73 maize straw samples showed differences in total potassium content, with Zheng 58 having higher levels. In comparisons between transgenic and non-transgenic straws, transgenic Zheng 58 maize straw carrying the CM8101 gene exhibited higher total nitrogen content than its non-transgenic counterpart, suggesting an enhanced capacity for nitrogen absorption or utilization. Transgenic B73 maize straw with the 2A-7 gene showed lower nitrogen content but higher potassium content, which may indicate changes in the metabolism or absorption mechanisms for nitrogen and potassium. These results provide baseline data for subsequent analyses of the impact of straw return on soil microbial communities.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMicrobial Alpha Diversity Analysis\u003c/h3\u003e\n\u003cp\u003eThis study assessed the impact of transgenic straw treatment on the Alpha diversity of soil microbial communities, including species richness index, Shannon diversity index, and Simpson diversity index (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results indicated that soil samples treated with straw exhibited significant differences in Alpha diversity compared to the untreated control (p-values were not indicated in the figure). However, comparisons of soil microbiota treated with GM straw to those treated with non-GM straw revealed no significant statistical differences in species richness, Shannon index, or Simpson index (with p-values ranging from 0.1 to 1.0), indicating that GM does not affect the Alpha diversity of soil microbial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs time progressed, the Alpha diversity of the soil microbial communities exhibited significant variations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Notably, in nearly all time-point comparisons, significant statistical variations were observed in alpha-diversity indexes, with alpha-diversity reaching its peak at the M6 time point. However, a significant decrease in alpha-diversity was noted at the M9 time point. This pattern may reflect the dynamic changes in soil alpha-diversity following straw decomposition. Specifically, the initial phase of straw decomposition might have provided additional nutrients, fostering an increase in microbial diversity, which led to the observed peak in alpha-diversity at the M6 time point. However, over time, these nutrients might have been progressively depleted by the microbial community, leading to a decline in microbial diversity at the M9 time point. These observations underscore the significant impact of sampling time on the alpha-diversity of soil microbial communities.\u003c/p\u003e \u003cp\u003eIn assessing the impact of straw treatment methods on soil microbial communities' alpha diversity, comparisons between mulching and deep plowing revealed notable differences. The Shannon diversity index showed a p-value of 0.04 and the Simpson index had a p-value of 0.08, indicating that straw treatment methods substantially influence soil microbial Alpha.\u003c/p\u003e \u003cp\u003eIn summary, straw treatment notably affected the Alpha diversity of soil microbial communities, yet no significant differences were observed statistically between GM and non-GM straw treatments. Concurrently, sampling time had a significant impact on the Alpha diversity of soil microbial communities, while the specific methods of straw treatment (mulching versus deep plowing) also exhibited notable influence.\u003c/p\u003e\n\u003ch3\u003eMicrobial Beta Diversity Analysis\u003c/h3\u003e\n\u003cp\u003ePCoA was utilized to assess beta diversity, revealing the impact of various factors on the distribution patterns of beta-diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the temporal dimension, the distinct separation between samples indicates that the composition of soil microbiota underwent significant changes over time, reflecting that time is a key factor affecting beta-diversity. The changes in the distribution of samples at different time points reflect the impact of straw decomposition in soil on the composition of microbial communities. In contrast, the treatment methods of mulching and deep plowing showed no significant differences in the microbial community structure on PCo1 and PCo2 axes, indicating that these treatment methods, while different in approach, result in similar impacts on microbial beta-diversity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding genotypes, the overlap of samples indicates that the straw treatments from different genotypes do not significantly alter the beta-diversity of the soil microbial community. Similarly, no notable distinction in beta-diversity was observed between samples on the level of GM versus non-GM, suggesting that the transgenic traits of the straw do not decisively alter the overall structure of the microbial community. This could be because the microbial communities are not particularly sensitive to the transgenic traits of the straw, or the transgenic traits do not significantly change the overall chemical composition of the straw, thereby exerting a limited impact on the microbial community.\u003c/p\u003e \u003cp\u003eOverall, these results accentuate the significant effect of temporal changes related to straw decomposition on soil microbial beta-diversity and also suggest the potential influences of treatment methods, genotypes, and GM status on the beta-diversity of soil microbial communities.\u003c/p\u003e\n\u003ch3\u003eDifferences in Microbial Community Composition\u003c/h3\u003e\n\u003cp\u003eThe phylum-level stacked bar chart represents the relative abundance of various microbial phylum across different samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). One month into the experiment (at sampling point M1), considerable differences in the composition of microbial phyla were observed among groups based on different genotypes and treatment methods. This indicates that GM straw and soil management strategies have important initial impacts on soil microbial diversity. Specifically, deep plowing treatment in Zheng 58 significantly decreased the abundance of Planctomycetes, whereas the same treatment in B73 markedly decreased the abundance of Acidobacteria, Chloroflexi, Thaumarchaeota, and Gemmatimonadetes, and increased the abundance of Bacteroidetes and Proteobacteria, indicating the distinct impacts of maize straw from different cultivars on the soil microbial community structure.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs time progressed to six months (sampling point M6), the microbial composition of the various treatment groups began to converge, suggesting that the soil microbial community composition is moving towards a new equilibrium after the initial disturbance. This convergence may indicate a certain resilience within the soil microbial communities, which are capable of adapting to environmental changes brought about by the cultivation of GM straw. By the nine-month mark of the experiment (sampling point M9), a reduction in the relative abundance of Proteobacteria was observed across all GM straw sample groups compared to non-GM groups, with proportions more closely aligning with initial conditions. These results reflect the impact of long-term straw return on soil ecological changes, which may be associated with factors such as the type of crops, GM traits, farming practices, and sampling time points.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe composite volcano plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) illustrates the significant variations in microbial community composition at the genus level due to different treatment methods and genetic modification. Additionally, a supplementary table is provided to display variations at the species level (Supplementary Material 1). Compared to mulching, deep-tillage samples exhibited significant abundance changes across numerous genera. In all non-transgenic samples, mulching significantly enriched \u003cem\u003eZancudomyces\u003c/em\u003e (\u003cem\u003eZancudomyces culisetae\u003c/em\u003e), while deep tillage led to an enrichment of \u003cem\u003eAcremonium\u003c/em\u003e (\u003cem\u003eAcremonium chrysogenum\u003c/em\u003e), \u003cem\u003eStachybotrys\u003c/em\u003e (\u003cem\u003eStachybotrys chartarum\u003c/em\u003e, \u003cem\u003eStachybotrys chlorohalonata\u003c/em\u003e), \u003cem\u003eGeosmithia\u003c/em\u003e (\u003cem\u003eGeosmithia morbida\u003c/em\u003e), \u003cem\u003eTolypocladium\u003c/em\u003e (\u003cem\u003eTolypocladium ophioglossoides\u003c/em\u003e, \u003cem\u003eTolypocladium paradoxum\u003c/em\u003e), \u003cem\u003eMetarhizium\u003c/em\u003e (\u003cem\u003eMetarhizium robertsii\u003c/em\u003e), \u003cem\u003eTrichoderma\u003c/em\u003e (\u003cem\u003eTrichoderma harzianum\u003c/em\u003e, \u003cem\u003eTrichoderma gamsii\u003c/em\u003e), \u003cem\u003eCordyceps\u003c/em\u003e, and \u003cem\u003ePochonia\u003c/em\u003e, among others. In transgenic samples, the genera enriched in deep tillage were similar to those found in non-transgenic deep-tilled samples, although differences may exist at the species level (such as \u003cem\u003eMetarhizium anisopliae\u003c/em\u003e). For the non-transgenic 2A-7 B73 maize straw soil samples, mulching treatment resulted in an enrichment of \u003cem\u003eHapalosiphon\u003c/em\u003e, \u003cem\u003eGloeocapsopsis\u003c/em\u003e, and \u003cem\u003eArdenticatena\u003c/em\u003e, while deep tillage led to an enrichment of genera such as \u003cem\u003eBacteriovorax\u003c/em\u003e, \u003cem\u003eAcremonium\u003c/em\u003e (\u003cem\u003eAcremonium chrysogenum\u003c/em\u003e), \u003cem\u003eStachybotrys\u003c/em\u003e (\u003cem\u003eStachybotrys chartarum\u003c/em\u003e), \u003cem\u003eMetarhizium\u003c/em\u003e (\u003cem\u003eMetarhizium robertsii\u003c/em\u003e), \u003cem\u003eStreptomyces\u003c/em\u003e (\u003cem\u003eStreptomyces fulvorobeus\u003c/em\u003e), and \u003cem\u003eXenorhabdus\u003c/em\u003e. In the 2A-7 transgenic B73 maize straw soil samples, mulching treatment enriched genera including \u003cem\u003eCandidatus Nitrosocaldus\u003c/em\u003e, \u003cem\u003eCandidatus Nitrosocosmicus\u003c/em\u003e, and \u003cem\u003eNitrososphaera\u003c/em\u003e, whereas deep tillage exhibited a similar enrichment of genera to that found in non-transgenic samples with some differences at the species level (such as \u003cem\u003eStachybotrys chlorohalonata\u003c/em\u003e, \u003cem\u003eMetarhizium anisopliae\u003c/em\u003e). In the non-transgenic CM8101 Zheng58 maize straw soil samples, the mulching treatment enriched genera such as \u003cem\u003eCyanobacterium\u003c/em\u003e, \u003cem\u003eDesulfurobacterium\u003c/em\u003e, \u003cem\u003eCandidatus Fonsibacter\u003c/em\u003e (\u003cem\u003eCandidatus Fonsibacter lacus\u003c/em\u003e), \u003cem\u003eCandidatus Thalassarchaeum\u003c/em\u003e, and \u003cem\u003eAchromatium\u003c/em\u003e, whereas deep tillage led to an enrichment of \u003cem\u003eEutypa\u003c/em\u003e (\u003cem\u003eEutypa lata\u003c/em\u003e), \u003cem\u003eHirsutella\u003c/em\u003e, \u003cem\u003eChrysoporthe\u003c/em\u003e, \u003cem\u003eArthrinium\u003c/em\u003e, and \u003cem\u003eOphiocordyceps\u003c/em\u003e. In the CM8101 transgenic Zheng58 maize straw soil samples, the mulching treatment resulted in the enrichment of genera including \u003cem\u003eActinomyces\u003c/em\u003e, \u003cem\u003eDesulfobacter\u003c/em\u003e, \u003cem\u003eCarboxydothermus\u003c/em\u003e, \u003cem\u003eAphanizomenon\u003c/em\u003e, \u003cem\u003eArcobacter\u003c/em\u003e, \u003cem\u003eElizabethkingia\u003c/em\u003e, and \u003cem\u003eOligella\u003c/em\u003e, while deep tillage enriched \u003cem\u003eTankvirus\u003c/em\u003e, \u003cem\u003eLysobacter\u003c/em\u003e, and \u003cem\u003eJasminevirus\u003c/em\u003e among others. Comparisons between non-transgenic and transgenic samples revealed significant enrichment of \u003cem\u003eMicrobulbifer\u003c/em\u003e, \u003cem\u003eMucilaginibacter\u003c/em\u003e, \u003cem\u003eCellvibrio\u003c/em\u003e, \u003cem\u003eGilvimarinus\u003c/em\u003e, \u003cem\u003eThalassocella\u003c/em\u003e, and \u003cem\u003eAgarilytica\u003c/em\u003e in non-transgenic samples, with no significant enrichment observed in the transgenic counterparts. In the non-transgenic versus transgenic comparison of 2A-7 maize, non-transgenic samples were notably enriched with \u003cem\u003ePhyllobacterium\u003c/em\u003e, \u003cem\u003eHydrocarboniclastica\u003c/em\u003e, and \u003cem\u003eAlishewanella\u003c/em\u003e, while no significant enrichment of any genus was detected in transgenic samples. Lastly, in the comparison between non-transgenic and transgenic CM8101 maize, non-transgenic samples showed significant enrichment of \u003cem\u003eDesulfurispira\u003c/em\u003e, \u003cem\u003eMicrobulbifer\u003c/em\u003e, \u003cem\u003eEndogone\u003c/em\u003e, \u003cem\u003ePhycomyces\u003c/em\u003e, and \u003cem\u003eErythromicrobium\u003c/em\u003e, whereas transgenic samples were enriched with \u003cem\u003eCandidatus Nitrosocosmicus\u003c/em\u003e, \u003cem\u003eCandidatus Nitrosocaldus\u003c/em\u003e, and \u003cem\u003eNitrososphaera\u003c/em\u003e. Overall, both transgenic and non-transgenic samples demonstrated similar shifts in microbial communities under the same treatment conditions. Moreover, the number of microbial changes induced by different management practices exceeded those caused by genetic modification. These findings suggest that management practices have a more significant impact on microbial diversity than genetic engineering.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the changes in microbial genera over time in GM and non-GM samples compared to the blank control (BLANK). Variations at the species level are detailed in Supplementary Material 2. At time point M1, non-GM samples exhibited a significant decrease in genera such as \u003cem\u003eCandidatus Nitrosocosmicus\u003c/em\u003e, \u003cem\u003eCandidatus Nitrosocaldus\u003c/em\u003e, and \u003cem\u003eThelephora\u003c/em\u003e (\u003cem\u003eThelephora ganbajun\u003c/em\u003e), while genera such as \u003cem\u003eMetarhizium\u003c/em\u003e (\u003cem\u003eMetarhizium anisopliae\u003c/em\u003e), \u003cem\u003eGeosmithia\u003c/em\u003e, \u003cem\u003ePurpureocillium\u003c/em\u003e (\u003cem\u003ePurpureocillium lilacinum\u003c/em\u003e), \u003cem\u003eRoseovarius\u003c/em\u003e, and \u003cem\u003eArthrospira\u003c/em\u003e were significantly enriched. In GM samples, genera like \u003cem\u003eChoiromyces\u003c/em\u003e and \u003cem\u003eThelephora\u003c/em\u003e (\u003cem\u003eThelephora ganbajun\u003c/em\u003e) were reduced, with an enrichment of \u003cem\u003eArcobacter\u003c/em\u003e, \u003cem\u003eTolypocladium\u003c/em\u003e (\u003cem\u003eTolypocladium ophioglossoides\u003c/em\u003e), \u003cem\u003eTrichoderma\u003c/em\u003e, \u003cem\u003ePochonia\u003c/em\u003e, \u003cem\u003eSulfurovum\u003c/em\u003e, \u003cem\u003eArthrospira\u003c/em\u003e, \u003cem\u003eFlavobacterium akiainvivens\u003c/em\u003e, \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e, etc. At time point M6, non-GM samples showed a notable decrease in genera like \u003cem\u003eCandidatus Nitrosocosmicus\u003c/em\u003e and \u003cem\u003eThelephora\u003c/em\u003e (\u003cem\u003eThelephora ganbajun\u003c/em\u003e), with an increase in \u003cem\u003ePelagicoccus\u003c/em\u003e, \u003cem\u003eCandidatus Rhabdochlamydia\u003c/em\u003e, \u003cem\u003eSaccharophagus\u003c/em\u003e, \u003cem\u003eCellvibrio\u003c/em\u003e, \u003cem\u003eThalassolituus\u003c/em\u003e, and \u003cem\u003eAzorhizobium\u003c/em\u003e. GM samples demonstrated a reduction in \u003cem\u003ePleurotus\u003c/em\u003e and \u003cem\u003eThelephora\u003c/em\u003e (\u003cem\u003eThelephora ganbajun\u003c/em\u003e), with an augmentation of \u003cem\u003eSaccharophagus\u003c/em\u003e, \u003cem\u003eFertoebacter\u003c/em\u003e, \u003cem\u003eCellvibrio\u003c/em\u003e, \u003cem\u003eAgarilytica\u003c/em\u003e, \u003cem\u003eTaibaiella\u003c/em\u003e, and \u003cem\u003eRoseovarius\u003c/em\u003e. By time point M9, both non-GM and GM samples revealed a marked decline in \u003cem\u003ePleurotus\u003c/em\u003e and \u003cem\u003eThelephora\u003c/em\u003e (\u003cem\u003eThelephora ganbajun\u003c/em\u003e). Non-GM samples were characterized by an enrichment of \u003cem\u003eSaccharophagus\u003c/em\u003e, \u003cem\u003eAgarilytica\u003c/em\u003e, \u003cem\u003eGilvimarinus\u003c/em\u003e, \u003cem\u003eTeredinibacter\u003c/em\u003e, \u003cem\u003eThalassocella\u003c/em\u003e, and \u003cem\u003eCellvibrio\u003c/em\u003e, whereas GM samples were abundant in \u003cem\u003eSaccharophagus\u003c/em\u003e, \u003cem\u003eTeredinibacter\u003c/em\u003e, \u003cem\u003eAgarilytica\u003c/em\u003e, \u003cem\u003eCellvibrio\u003c/em\u003e, \u003cem\u003eAzorhizobium\u003c/em\u003e, and \u003cem\u003eDesulfatiglans\u003c/em\u003e. Overall, the microbial diversity changes were most abundant at the M6 sampling point. By the M9 sampling point, this diversity had diminished, potentially correlating with the straw decomposition process. Based on the number of significantly changed genera, it can be inferred that straw decomposition might proceed more rapidly in GM straw, or that the soil microbial community shifts in GM straw soils are less pronounced compared to non-GM straw soils relative to blank soil.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the characteristic microbial communities identified by LEfSe analysis, a heatmap was constructed using the average proportions of these microbes in each group of samples. To enhance clarity of the inter-group variations, the data were normalized. The heatmap (see Supplementary Material 3) reveals that at the M1 timepoint, samples from deep tilling treatments of both transgenic and non-transgenic Zheng58 maize were markedly enriched in the Actinobacteria class. Cover treated samples of transgenic CM8101 maize collected at M1 and M6 timepoints showed a significant enrichment of microbes belonging to the classes Cytophagia, Anaerolineae, and Gemmatimonadetes. At the M6 timepoint, the 2A-7 transgenic samples were distinctly enriched in Deltaproteobacteria. Additionally, Gammaproteobacteria and Betaproteobacteria were predominantly distinguished in samples from the M1 timepoint. Overall, samples from the M1 timepoint demonstrated certain degrees of distinction in microbial composition compared to those from M6 and M9 timepoints, which may be attributed to the differential impacts of treatment methods on soil microbial community structure associated with the timing of sampling.\u003c/p\u003e\n\u003ch3\u003ePredictive Analysis of Microbial Functionality\u003c/h3\u003e\n\u003cp\u003eUpon analyzing the impact of straw treatment on soil microbial communities, significant differences in the presence of KEGG metabolic pathways among treatment groups were observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Compared to the untreated soil, straw treatment predominantly influenced pathways associated with carbon fixation and cycling, such as carbon fixation pathways in prokaryotes, metabolism of 2-oxocarboxylic acids, and metabolism of acetic acid and dicarboxylic acids, as well as pathways related to protein synthesis and degradation, including RNA polymerase, ribosome, and protein export. At the M1 time point, the samples demonstrated significant presence in energy and nutrient metabolism, such as metabolism of fructose and mannose, galactose metabolism, and pyruvate metabolism, indicating that straw provides a rich carbon source for soil microbes. Additionally, the cycling of sulfur and nitrogen was significantly affected, including sulfur metabolism and amino acid metabolism, showcasing the microbial transformation and utilization of nitrogen and sulfur sources during straw decomposition. The presence of functions in the synthesis and breakdown of secondary metabolites (e.g., tryptophan metabolism, porphyrin, and chlorophyll metabolism), as well as nucleic acid and energy metabolism (e.g., purine metabolism, pyrimidine metabolism, one carbon pool by folate), reflects the rapid microbial response to straw addition, involving key biological processes such as biosynthesis, energy metabolism, and environmental adaptation. As time progressed to the M6 and M9 sampling points, the differences in metabolic pathway presence among the samples began to diminish. At the M6 time point, samples showed an enrichment in pathways related to amino acid biosynthesis, fatty acid biosynthesis, and starch and sucrose metabolism, suggesting that the microbial community may have reached a new dynamic equilibrium. By the M9 time point, samples exhibited enrichment in pathways such as butyrate metabolism, fatty acid degradation, nitrogen metabolism, and pyruvate metabolism, reflecting the microbial community's adaptation to long-term straw decomposition. Furthermore, differences in KEGG functional presence were observed between the mulching and deep plowing treatment samples. Although the deep plowing treatment resulted in a significantly richer microbial community and higher alpha diversity compared to the mulching treatment, the KEGG functional diversity was richer in the mulching samples. This discrepancy may be attributed to the distinct microenvironments provided by the mulching and deep plowing treatments. While deep plowing enhances microbial species richness by thoroughly mixing straw with the soil, mulching may lead to a specialization in metabolic functions, as reflected in the enrichment of KEGG functions. Overall, straw treatment significantly affected the presence of KEGG metabolic pathways in soil microbial communities, revealing the facilitative role of straw decomposition on soil microbial functional diversity and ecosystem stability. It also demonstrated the varying impacts of different treatment methods and time on the functional potential of soil microbial communities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results of the eggNOG Pathway analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) indicated that at the M1 sampling time point, a high level of potential functionality was noted in multiple metabolic pathways, particularly nucleotide transport and metabolism (F), inorganic ion transport and metabolism (P), and carbohydrate transport and metabolism (G). This heightened presence is likely a response to the increased availability of substrates following the addition of straw. By the M6 time point, while the differential presence of some pathways diminished, an increase in the presence of functions related to cell wall, membrane, and envelope biogenesis (M), as well as cell motility (N), suggested a possible trend toward stabilization of the soil microbial community over time. However, at the M9 time point, enhanced presence of functions was again observed in pathways such as amino acid transport and metabolism (E), lipid transport and metabolism (I), secondary metabolites biosynthesis, transport, and catabolism (Q), and signal transduction mechanisms (T), reflecting the microbial community's adaptation to the prolonged decomposition of straw. In transgenic samples, a particularly pronounced presence of functions was evident in pathways involved in defense mechanisms (V), amino acid transport and metabolism (E), lipid transport and metabolism (I), and secondary metabolites biosynthesis, transport, and catabolism (Q). Collectively, these data reveal the specific impacts of straw management practices on soil microbial functional diversity in both transgenic and non-transgenic crops. Additionally, the findings illustrate the dynamic changes in microbial community functional potential across different time points.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, focusing on transgenic maize 2A-7 and its non-transgenic counterpart B73, as well as transgenic maize CM8101 and its non-transgenic counterpart Zheng58, two treatment methods were applied: straw mulching and deep plowing. The aim was to investigate the specific impact of transgenic straw return to the field on the ecology and function of soil microbiota. The results indicate that the impact of transgenic straw on the diversity and community structure of soil microbiota is limited compared to the effects of time progression and treatment methods. Microbial diversity peaked at the M6 time point, while microbial function exhibited the richest variation at the M1 time point. Deep plowing promoted an increase in biodiversity, whereas mulching led to a concentration of microbial functions. These findings emphasize the decisive role of treatment methods and time in shaping soil microbial ecosystems, highlighting that the direct impact of transgenic traits on soil microbiota is relatively limited.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that during straw return processes, different crop straws exhibit unique patterns of microbial diversity and activity at various time points. For instance, research on rice straw return found the highest alpha diversity of microbes at 137 days, with microbial activity being more vigorous in the early phase (0\u0026ndash;20 days) and slowing down in the later phase (104\u0026ndash;137 days) \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Similarly, an oat straw return study observed that microbial diversity was higher in the sixth month than in the first month, although microbial activity measured by soil respiration rate was higher initially and then decreased \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This study revealed that microbial diversity at the M6 time point (180 days) was significantly higher than at the M1 time point (30 days), and microbial functional activity peaked at M1 before showing a declining trend. This could be associated with the rapid response and nutrient release during the initial phase of straw decomposition, which enhances specific microbial activities. As time progresses and the easily decomposable components of straw are gradually consumed, the decomposition process shifts towards more complex organic matter, necessitating a more diverse microbial involvement. By the 180-day mark, a considerable portion of the straw had been decomposed, with other studies indicating a decomposition rate of about 50% \u003csup\u003e14\u003c/sup\u003e, leading to a relative decrease in available nutrients. However, by the 270-day mark (M9 time point), despite further decomposition, the reduction in available nutrients may have led to a decrease in microbial diversity, reflecting the dynamic changes and adjustments within the microbial community during the straw decomposition process. Specifically, across all time points when compared to control soil, the reduction of \u003cem\u003eThelephora ganbajun\u003c/em\u003e might indicate an increase in competitive pressure during straw decomposition, leading to the loss of survival conditions for this species in the new environment. In non-transgenic samples, the absence of \u003cem\u003eCandidatus_Nitrosocosmicus\u003c/em\u003e and \u003cem\u003eCandidatus_Nitrosocaldus\u003c/em\u003e suggests alterations in the ammonia oxidation process and nitrogen cycle pathways \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. The enrichment of known biocontrol fungi, \u003cem\u003eMetarhizium\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003ePurpureocillium\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, in non-transgenic M1 samples could imply that the straw itself may offer certain resistance to pests. Meanwhile, in transgenic M1 samples, the enrichment of biocontrol fungi such as \u003cem\u003eTrichoderma\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003ePochonia\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e further underscores the positive role of transgenic straw return in enhancing the potential for biological control in soil. At the M6 and M9 time points, the consistency of many enriched genera, such as the increase of \u003cem\u003eSaccharophagus\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eCellvibrio\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, indicates that straw return has strengthened the soil's cellulose decomposition capability, facilitating nutrient cycling and the degradation of complex organic matter in straw; the increase in \u003cem\u003eAgarilytica\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e might reflect the activation of specific organic matter decomposition pathways in the soil. During the initial stages of straw return to the field, the observed high levels of energy and nutrient metabolism activity, particularly in nucleotide transport and metabolism, inorganic ion transport and metabolism, and carbohydrate transport and metabolism, may be a direct response of the microbial community to the abundant nutritional resources provided by the added straw. As time progresses, towards the M6 and M9 time points, the microbial community adapts to the new environment, displaying diverse metabolic activities such as enhanced cellular structure and energy metabolism. This reflects the community's evolution towards a stable state and its adaptation to the decomposition of more complex organic materials. These changes reveal the dynamic impact of straw return on the functional diversity of soil microbial communities, highlighting the significant role of time in the process of straw decomposition.\u003c/p\u003e \u003cp\u003eIn discussing the impacts of deep plowing and mulching treatments, it is noteworthy to consider the significant influence of deep plowing on the soil microbial communities. Regardless of whether in non-transgenic or transgenic deep plowing treatment samples, a variety of fungi and bacteria such as \u003cem\u003eAcremonium\u003c/em\u003e, \u003cem\u003eStachybotrys\u003c/em\u003e, \u003cem\u003eMetarhizium\u003c/em\u003e, and \u003cem\u003eTrichoderma\u003c/em\u003e were enriched. These fungi are commonly associated with the decomposition of organic matter and nutrient cycling \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, indicating that deep plowing treatment facilitated the activity of microorganisms closely related to straw decomposition. \u003cem\u003eParticularly\u003c/em\u003e, \u003cem\u003eTrichoderma\u003c/em\u003e and \u003cem\u003eMetarhizium\u003c/em\u003e, which are widely used in biological control \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, their enrichment may suggest that deep plowing treatment helps enhance the potential for natural disease management in the soil. Overall, these results demonstrate that deep plowing treatment increased the microbial diversity related to straw decomposition in the soil, increasing the relative abundance of some beneficial microorganisms. This may positively impact soil fertility and plant health. However, further studies are necessary to directly link these microbial changes with specific soil health indicators and plant performance.\u003c/p\u003e \u003cp\u003eThis study investigated the impacts of straw return from GM and non-GM maize through two different management practices, mulching and deep incorporation, on soil microbial community composition and function. These results indicate that, compared to GM traits, the method and timing of treatment played a more significant role in shaping the composition and function of soil microbial communities. Particularly, deep incorporation significantly enhanced microbial diversity and enriched microorganisms closely associated with straw decomposition, potentially benefiting soil fertility and plant health. Additionally, this research revealed dynamic changes in soil microbial communities in response to straw return, adapting to different stages of straw decomposition. Notably, the impact of GM straw on soil microbes was relatively limited, an important finding for assessing the sustainable application of GM crops in agricultural ecosystems. It suggests that returning GM crop straw is unlikely to adversely affect soil microbial ecology, supporting the integration of GM crops into sustainable agricultural practices without compromising soil microbial diversity and function. In summary, this study underscores the importance of considering management practices and timing in optimizing agricultural practices, while also providing scientific evidence for further research into the role of GM crops within agricultural ecosystems.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eMaterials and Location\u003c/h2\u003e \u003cp\u003eFour types of maize straw were utilized in this study: (1) non-transgenic B73 maize straw, (2) B73 maize straw GM with the 2A-7 gene, (3) non-transgenic Zheng 58 maize straw, and (4) Zheng 58 maize straw GM with the CM8101 gene. Designate the non-transgenic maize straw as CK.\u003c/p\u003e \u003cp\u003eThe experimental site was located at the Transgenic Plant Field Testing Base of Jilin Academy of Agricultural Sciences, situated in Gongzhuling City, Jilin Province, which is a proprietary testing ground of the Jilin Academy of Agricultural Sciences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGrouping\u003c/h2\u003e \u003cp\u003eThis experiment was conducted within planting areas of no less than 150 square meters, managed using conventional farming practices and without the use of insecticides throughout the entire growth period. The experiment was replicated six times with the following treatments:\u003c/p\u003e \u003cp\u003eNo Straw Added Treatment: Soil samples adjusted to the appropriate moisture level were placed in beakers without the addition of maize straw, labeled as 'BLANK'.\u003c/p\u003e \u003cp\u003eMulching Treatment: Soil samples adjusted to the appropriate moisture level were placed in beakers, and then a uniform layer of maize straw was spread on the top, labeled as 'M'.\u003c/p\u003e \u003cp\u003eDeep Plowing Treatment: Soil samples adjusted to the appropriate moisture level were thoroughly mixed with maize straw, labeled as 'D'.\u003c/p\u003e \u003cp\u003eThe experimental sampling times were set as follows: 'M1' denotes sampling conducted 1 month (30 days) after the experiment commenced; 'M6' represents sampling conducted 6 months (180 days) after the start of the experiment; 'M9' indicates sampling conducted 9 months (270 days) post-experiment initiation.\u003c/p\u003e \u003cp\u003eFollowing the naming conventions, which denote the timing of sampling (M1, M6, M9), gene type (2A_7, CM8101), treatment method (M for mulching, D for deep plowing), and sample status (CK for non-transgenic, T for transgenic), this study comprised 25 distinct groups: BLANK, M1_2A_7_M_CK, M1_2A_7_M_T, M1_2A_7_D_CK, M1_2A_7_D_T, M1_CM8101_M_CK, M1_CM8101_M_T, M1_CM8101_D_CK, M1_CM8101_D_T, M6_2A_7_M_CK, M6_2A_7_M_T, M6_2A_7_D_CK, M6_2A_7_D_T, M6_CM8101_M_CK, M6_CM8101_M_T, M6_CM8101_D_CK, M6_CM8101_D_T, M9_2A_7_M_T, M9_2A_7_M_CK, M9_2A_7_D_T, M9_2A_7_D_CK, M9_CM8101_M_CK, M9_CM8101_M_T, M9_CM8101_D_CK, M9_CM8101_D_T.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSoil and Straw Sample Component Analysis\u003c/h2\u003e \u003cp\u003eSoil moisture content was determined using the drying method according to the People's Republic of China national standard (NYT52-1987). The procedure involved weighing approximately 10 grams of soil to determine its wet weight, then drying the sample in an oven at 105\u0026deg;C until constant weight was achieved. After cooling in a desiccator to room temperature, the dry weight was measured. Moisture content was calculated using the formula: Moisture percentage = [(Wet weight - Dry weight) / Wet weight] \u0026times; 100%.\u003c/p\u003e \u003cp\u003eTotal potassium and phosphorus contents in the soil were determined based on the forestry industry standards LY_T 1234\u0026ndash;2015 and LY_T 1232\u0026ndash;2015, respectively, using the alkali fusion method. The procedure included mixing 0.25 g of soil with 2 g of sodium hydroxide, then heating in a high-temperature furnace to 750\u0026deg;C and holding for 15 minutes. After cooling, the sample was treated with water and sulfuric acid to adjust the pH of the solution to an appropriate level before filtration. The filtrate was then directly measured using a photometer for potassium or phosphorus content.\u003c/p\u003e \u003cp\u003eTotal nitrogen content was determined according to the forestry industry standard LYT1228-2015 using the Kjeldahl method. The soil sample was digested with concentrated sulfuric acid to convert all nitrogen to ammonium form, which was then alkalized and distilled. The ammonia distilled was absorbed by boric acid solution and quantified by titration with a standard acid solution. For samples containing nitrate and nitrite nitrogen, pre-treatment included oxidation with potassium permanganate and reduction with iron powder.\u003c/p\u003e \u003cp\u003eStraw samples, prepared as dry powders, were analyzed according to the agricultural industry standard NY525-2012. Total nitrogen was determined using the Kjeldahl method, total phosphorus by the molybdenum blue spectrophotometric method, and total potassium by the flame photometric method. In the molybdenum blue method, samples were digested with a mixture of concentrated nitric and perchloric acids, reacting with ammonium molybdate and ascorbic acid under acidic conditions to form a blue complex, the absorbance of which was measured at 880 nm. For potassium, the digested samples were diluted appropriately and the potassium content was measured at specific wavelengths using a flame photometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTreatment and Sample Collection\u003c/h2\u003e \u003cp\u003eIn this experiment, 500 mL beakers were filled with 120 g of soil, which had never been planted with GM maize. All soil was finely ground, sieved, air-dried, and weighed. The amount of straw was calculated based on the typical straw return amount in black soil regions (12,000 kg\u0026middot;hm-2) and the average soil bulk density of the plow layer (1.1 g\u0026middot;cm-3), initially resulting in a soil-to-straw ratio of 281:1. However, as adding only 0.4 g of straw to each 120 g of soil was insufficient for covering or mixing, following recommendations, the ratio was adjusted to 5.6:1. Some studies have used a straw addition ratio of 10%. The corresponding maize straw was dried, crushed, thoroughly mixed, and the remaining powder was stored frozen. Before the experiment began, the air-dried soil samples were analyzed for organic carbon content, various nutrient contents, and pH values, and the sieved soil samples underwent microbial sequencing. Each beaker, containing 120 g of air-dried soil covered with a breathable plastic film, was adjusted to field capacity moisture content (about 20%) with distilled water, and it was determined through gradient experiments to periodically spray each beaker with 20 mL of water to maintain moisture. Dynamic sampling points were set for 30, 90, 180, and 270 days, with straw being removed from the soil prior to sampling. Samples were quickly placed in ice boxes and then transferred to a -80\u0026deg;C freezer awaiting sequencing analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiome Data Acquisition\u003c/h2\u003e \u003cp\u003eMetagenomic sequencing and preliminary data processing were conducted at Shanghai OE Biotech Co., Ltd. Approximately 0.5 g of fresh soil sample (stored at -80\u0026deg;C) was used for microbial genomic DNA extraction using the MagPure Soil DNA KF Kit, according to the manufacturer's protocol. After DNA extraction, DNA quality and quantity were assessed by agarose gel electrophoresis and a NanoDrop spectrophotometer.\u003c/p\u003e \u003cp\u003eSequencing libraries were prepared following the standard procedures provided by the sequencing facility, without targeted PCR amplification steps. Trimmomatic \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e was utilized for quality control of the sequencing data to ensure data integrity. Then, the assembly of metagenomic sequences was carried out using MEGAHIT \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which is based on the De Bruijn graph principle, resulting in the construction of Contigs. Only Contigs exceeding 500 bp in length were selected for subsequent analysis.\u003c/p\u003e \u003cp\u003eUpon the assembly of genomes, Prodigal \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e was applied to predict Open Reading Frames (ORFs) from the Contigs, and the predicted ORFs were then translated into amino acid sequences. To acquire a non-redundant initial Unigene set, the ORF prediction results from all samples were subjected to redundancy removal using CD-HIT \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, with an identity threshold of 95% and a coverage threshold of 90%, resulting in a non-redundant gene set. Representative sequences from each cluster were selected as Unigenes.\u003c/p\u003e \u003cp\u003eFor functional annotation, the non-redundant gene sequences (amino acid sequences) were aligned using DIAMOND (v0.9.7) against databases including NR, KEGG\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, COG\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, SWISSPROT\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and GO\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. BLAST alignment was performed with an expectation value (e-value) threshold of 1e-10, selecting the best-hit results for functional annotation.\u003c/p\u003e \u003cp\u003eFinally, Bowtie2 \u003csup\u003e34\u003c/sup\u003e was employed to align the clean reads from each sample with the non-redundant gene set (with 95% identity), thereby obtaining gene abundance information in each sample. This sets the foundation for subsequent bioinformatics analysis and functional annotation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis and Visualization\u003c/h2\u003e \u003cp\u003eData analysis and visualization were conducted in the R programming environment, version 4.3.1. Alpha diversity indices, including species richness, Shannon index, and Simpson index, were calculated based on species-level abundance data using the vegan package \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Principal Coordinates Analysis (PCoA) was performed based on Bray\u0026ndash;Curtis distances calculated using the vegdist function from the vegan package, and ordination was conducted with the pco function from the labdsv package \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The EnhancedVolcano package \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e was employed for volcano plot generation, while the pheatmap package \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e was used for heatmap creation.\u003c/p\u003e \u003cp\u003eP-values were calculated using the Wilcox test. Unless otherwise stated, p-values less than 0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Jilin Scientific and Technological Development Program, China (Grant number: 20230508091RC). All sources of funding for all authors relevant to the content of this manuscript have been disclosed.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYanbo Xie: investigation, experiments, writing\u0026mdash;original draft preparation; Jun-Yan Xiang: data analysis, visualization, writing\u0026mdash;original draft preparation; Likun Long: validation, writing\u0026mdash;review and editing; Yue Ma, Zhenjuan Xing, Ling Wang, and Chunyu Shao: experiments; Na Liu: conceptualization, supervision; Feiwu Li: conceptualization, supervision, project administration, funding acquisition.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the findings of this study are provided within the manuscript and its supplementary materials, as well as the NCBI Sequence Read Archive (SRA) repository, under accession number PRJNA1203889 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1203889/), and are available on request from the corresponding author (Feiwu Li, [email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEllert, B. 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EnhancedVolcano: Publication-Ready Volcano Plots with Enhanced Colouring and Labeling. https://doi.org/10.18129/B9.bioc.EnhancedVolcano (2023).\u003c/li\u003e\n\u003cli\u003eKolde,R.Pheatmap:PrettyHeatmaps.https://CRAN.R-project.org/package=pheatmap (2019).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Soil Microbiology, Genetically Modified Crops, Straw Decomposition, Metagenomic Sequencing, Agricultural Sustainability","lastPublishedDoi":"10.21203/rs.3.rs-5612017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5612017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding the impact of genetically modified (GM) crop straw return on soil ecosystems is crucial as GM crops become more prevalent. This study assesses the effects of straw mulching and deep plowing on soil microbial communities from GM and non-GM maize, highlighting potential ecological impacts. High-throughput metagenomic sequencing was utilized to analyzed the microbial community structure and functional genes in soil samples collected at different times (30, 180, and 270 days) after straw mulching and deep plowing treatments. The study included insect-resistant transgenic maize varieties 2A-7 and CM8101 and their non-transgenic counterparts B73 and Zheng58. Different treatment methods significantly affect soil microbial alpha-diversity and beta-diversity, with deep plowing resulting in higher alpha-diversity compared to mulching, and the 180-day mark exhibiting the highest alpha-diversity across all sampling times. Early straw treatment prompted a rapid microbial response to nutrient availability, with notable changes in diversity and function over time. Straw treatments notably altered soil microbial functions, especially in carbon cycling and nutrient metabolism. Interestingly, the microbial effects of GM versus non-GM maize straw were similar, suggesting crop residue type under consistent soil management practices might not significantly alter microbial community structures. The methods and timing of straw treatments have a significant impact on soil microbial communities, surpassing the differences between GM and non-GM straw. These findings highlight the importance of straw management practices for sustainable agricultural ecosystem management.\u003c/p\u003e","manuscriptTitle":"Impact of Different Treatment Methods and Timings on Soil Microbial Communities with Transgenic Maize Straw Return","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 04:38:55","doi":"10.21203/rs.3.rs-5612017/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-20T01:04:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-01T03:43:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214979424855023782423987569668443522391","date":"2025-04-07T13:27:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-07T07:55:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-27T04:26:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-20T00:44:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e38e1073-5cc2-4041-a33d-f42b730d137a","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46901110,"name":"Biological sciences/Microbiology/Environmental microbiology"},{"id":46901111,"name":"Biological sciences/Genetics"},{"id":46901112,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-07-14T15:59:50+00:00","versionOfRecord":{"articleIdentity":"rs-5612017","link":"https://doi.org/10.1038/s41598-025-09851-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-10 15:57:11","publishedOnDateReadable":"July 10th, 2025"},"versionCreatedAt":"2025-04-10 04:38:55","video":"","vorDoi":"10.1038/s41598-025-09851-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-09851-w","workflowStages":[]},"version":"v1","identity":"rs-5612017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5612017","identity":"rs-5612017","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}