High-throughput Technologies Research Articles

Metadata-guided feature disentanglement for functional genomics.

With the development of high-throughput technologies, genomics datasets rapidly grow in size, including functional genomics data. This has allowed the training of large Deep Learning (DL) models to predict epigenetic readouts, such as protein binding or histone modifications, from genome sequences. However, large dataset sizes come at a price of data consistency, often aggregating results from a large number of studies, conducted under varying experimental conditions. While data from large-scale consortia are useful as they allow studying the effects of different biological conditions, they can also contain unwanted biases from confounding experimental factors. Here, we introduce Metadata-guided Feature Disentanglement (MFD)-an approach that allows disentangling biologically relevant features from potential technical biases. MFD incorporates target metadata into model training, by conditioning weights of the model output layer on different experimental factors. It then separates the factors into disjoint groups and enforces independence of the corresponding feature subspaces with an adversarially learned penalty. We show that the metadata-driven disentanglement approach allows for better model introspection, by connecting latent features to experimental factors, without compromising, or even improving performance in downstream tasks, such as enhancer prediction, or genetic variant discovery. The code will be made available at https://github.com/HealthML/MFD.

Pseudogene: Relevant or Irrelevant?

With the advancement of high-throughput technologies, the pivotal role of non-coding RNA (ncRNA) as a master regulator of various biological functions has become increasingly apparent. Historically considered non-functional and labeled as "junk DNA," pseudogenes can be transcribed into RNA, indicating a potential role similar to ncRNAs. Recent research suggests that some pseudogenes can encode functional peptides or proteins. A growing body of evidence has revealed that pseudogenes and their derived functional molecules are involved in various biological processes and can serve as prognostic markers in cancers. This review comprehensively summarizes and discusses the current understanding of the functional roles of pseudogenes and their derived molecules in biological functions.

Comparison of the Intestinal Microbiota Composition and Function of Red Claw Crayfish (Cherax quadricarinatus) Cultured in Ponds and Rice Fields

The growth environment significantly influences the intestinal microbiota of aquatic organisms. We investigated the composition and functional differences in the intestinal microbiota of red claw crayfish (Cherax quadricarinatus) in rice fields (RB) and ponds (PB) by 16S rDNA high-throughput sequencing technology. The results indicate that the Shannon, Simpson, Sobs, Chao1, and ACE indices of PB are all higher than those of RB, demonstrating greater diversity and richness of intestinal microbiota. The dominant phyla in the intestinal microbiota of the Cherax quadricarinatus were Proteobacteria, Tenericutes, and Firmicutes. Tenericutes and Proteobacteria were significantly more abundant in the RB than in the PB, while Planctomycetes and Firmicutes were significantly more abundant in the PB than in the RB. The results of network correlation analysis indicate that Proteobacteria and Firmicutes exhibit strong connectivity with other microbial groups in the gut microbiota of Cherax quadricarinatus, showing significant centrality. They play an important role in the interactions within the gut microbiota community. The dominant bacterial genera in the Cherax quadricarinatus’s gut were Citrobacter, Candidatus_Bacilloplasma, and Clostridium_sensu_stricto_1. The abundance of the genus Clostridium was significantly higher in the PB than in the RB, whereas the abundance of Candidatus_Hepatoplasma and Vibrio was significantly lower in the PB than in the RB. The prediction function of KEGG enrichment showed that the abundance of Amino acid metabolism, Biosynthesis of Other Secondary Metabolites, Transport and Catabolism, Cancers, and Nervous System, Substance Dependence were significantly higher in the PB, while the infectious diseases pathway was enriched in the RB. In summary, our results revealed significant differences in the composition and diversity of intestinal microbiota in the Cherax quadricarinatus between rice paddy and pond farming environments. The intestinal microbiota of the Cherax quadricarinatus grown in pond environments exhibit higher diversity and stability, manifested by an increase in beneficial bacteria abundance and a decrease in opportunistic pathogens. These findings significantly improve understanding of the complex relationship among Cherax quadricarinatus, intestinal microbiota, and the environment.

Correlative expression of exosomal miRNAs in chemotherapy resistance of triple-negative breast cancer: An observational study.

Drug resistance in tumors is the primary contributor to clinical treatment failures, and aberrant expression of small RNA molecules, specifically microRNAs (miRNAs), in tumor tissues is intricately associated with drug resistance. The aim of this study is to investigate the targets and mechanisms through which exosomal miRNAs from triple-negative breast cancer (TNBC) regulate chemotherapy resistance in tumor cells. Utilizing high-throughput sequencing technology, we conducted exosomal miRNA sequencing on serum samples obtained from TNBC patients who were either sensitive or resistant to AC-sequential T chemotherapy. Subsequently, we identified and screened differentially expressed miRNAs. The observed differences in miRNA expression were further validated through quantitative reverse transcription-polymerase chain reaction. In comparison to TNBC patients who exhibited sensitivity to the AC-sequential T regimen chemotherapy, we identified significant differences in the expression of 85 miRNAs within serum exosomes of patients displaying chemotherapy resistance. Furthermore, we observed a substantial difference in the expression of hsa-miR-6831-5p between TNBC patients who were responsive to chemotherapy and those who were drug-resistant and underwent treatment with the AC-sequential T regimen. hsa-miR-6831-5p holds the potential to serve as a diagnostic marker for assessing the chemosensitivity of the AC-sequential T regimen in TNBC.

Changes in the microbial community structure and diversity during the electricity generation process of a microbial fuel cell with algal-film cathode

Abstract A two-chamber microbial fuel cell (MFC) with algal-film cathode was constructed. It showed good electric-generating performance with three electric-generating stages: start-up, development, and stable. An average output voltage reached ~0.412 V during the stable period. A maximum power density during continuous operation was 19.76 mW/m2. Bacterial samples were collected from the anode in the three stages (A1, A2, and A3), and their community structure and diversity were analyzed using Illumina MiSeq high-throughput sequencing technology. A total of 4238 operational taxonomic units were identified based on the number of taxa. At the phylum level, Proteobacteria and Bacteroidetes played a dominant role in the three stages and increased significantly during electricity generation. Compared with A1, the relative abundances of Proteobacteria in A2 and A3 increased by 23.30% and 32.06%, respectively, whereas those of Bacteroidetes in A2 and A3 increased by 5.56% and 14.50%, respectively. At the genus level, there were differences in the composition of bacterial communities among the three stages. Acinetobacter and Chlorobium became the dominant genera in A2, replacing Nitrospira and norank_f__Saprospiraceae in A1, and Sphingobacterium and Ochrobactrum became the dominant genera in A3. According to the sample cluster and principal component analyses, A1 was clustered into one class, and A2 and A3 were clustered into a second class. This work revealed bacterial community succession at the anode of an algal-film cathode MFC during the electricity generation process, which provides a theoretical basis for the subsequent promotion of electricity generation by algal-film cathode MFCs.

Signature Search Polestar: a comprehensive drug repurposing method evaluation assistant for customized oncogenic signature.

The burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, have been instrumental in anti-cancer drug screening and identifying mechanisms of action without relying on prior knowledge. However, various studies have found that different SSMs exhibit varying performance across pharmacotranscriptomic datasets. In addition, the size of the oncogenic signature can also significantly impact the result of drug repurposing. Therefore, finding the optimal SSMs and customized oncogenic signature for a specific disease remains a challenge. To address this, we introduce Signature Search Polestar (SSP), a webserver integrating the largest pharmacotranscriptomic datasets of anti-cancer drugs from LINCS L1000 with five state-of-the-art SSMs (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, and Application. Benchmark uses two indices, Area Under the Curve and Enrichment Score, based on drug annotations to evaluate SSMs at different oncogenic signature sizes. Robustness, applicable when drug annotations are insufficient, uses a performance score based on drug self-retrieval for evaluation. Application provides three screening strategies, single method, SS_all, and SS_cross, allowing users to freely utilize optimal SSMs with tailored oncogenic signature for drug repurposing. SSP is free at https://web.biotcm.net/SSP/. The current version of SSP is archived in https://doi.org/10.6084/m9.figshare.26524741.v1, allowing users to directly use or customize their own SSP webserver.

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Data Integration and Management in Bioinformatics

Bioinformatics is an interdisciplinary field that combines biology, computer science, and information technology to understand and analyze biological data. With the development of high-throughput sequencing technology and other biotechnologies, the speed and scale of biological data generation are unprecedented. Effectively integrating and managing these data has become a significant challenge. This paper explores the current state of bioinformatics data integration and management, the challenges faced, commonly used methods and tools, and future development directions.

Next-generation sequencing in Charcot-Marie-Tooth: a proposal for improvement of ACMG guidelines for variant evaluation

BackgroundThe application of massive parallel sequencing technologies in the molecular analysis of Charcot-Marie-Tooth (CMT) has enabled the rapid and cost-effective identification of numerous potentially significant variants for diagnostic purposes. The...

Bioinformatics advances in eccDNA identification and analysis.

Extrachromosomal circular DNAs (eccDNAs) are a unique class of chromosome-originating circular DNA molecules, which are closely linked to oncogene amplification. Due to recent technological advances, particularly in high-throughput sequencing technology, bioinformatics methods based on sequencing data have become primary approaches for eccDNA identification and functional analysis. Currently, eccDNA-relevant databases incorporate previously identified eccDNA and provide thorough functional annotations and predictions, thereby serving as a valuable resource for eccDNA research. In this review, we collected around 20 available eccDNA-associated bioinformatics tools, including identification tools and annotation databases, and summarized their properties and capabilities. We evaluated some of the eccDNA detection methods in simulated data to offer recommendations for future eccDNA detection. We also discussed the current limitations and prospects of bioinformatics methodologies in eccDNA research.

Integrative in silico approaches to analyse microRNA-mediated responses in human diseases.

Advancements in sequencing technologies have facilitated omics level information generation for various diseases in human. High-throughput technologies have become a powerful tool to understand differential expression studies and transcriptional network analysis. An understanding of complex transcriptional networks in human diseases requires integration of datasets representing different RNA species including microRNA (miRNA) and messenger RNA (mRNA). This review emphasises on conceptual explanation of generalized workflow and methodologies to the miRNA mediated responses in human diseases by using different in silico analysis. Although,there have been many prior explorations in miRNA-mediated responses in human diseases, the advantages, limitations and overcoming the limitation through different statistical techniques have not yet been discussed. This review focuses on miRNAs as important gene regulators in human diseases, methodologies for miRNA-target gene prediction and data driven methods for enrichment and network analysis for miRnome-targetome interactions. Additionally, it proposes an integrative workflow to analyse structural components of networks obtained from high-throughput data. This review explains how to apply the existing methods to analyse miRNA-mediated responses in human diseases. It addresses unique characteristics of different analysis, its limitations and its statistical solutions influencing the choice of methods for the analysis through a workflow. Moreover, it provides an overview of promising common integrative approaches to comprehend miRNA-mediated gene regulatory events in biological processes in humans. The proposed methodologies and workflow shall help in the analysis of multi-source data to identify molecular signatures of various human diseases.

Effects of Meyerozyma guilliermondii PJ15 on the biocontrol of Penicillium crustosum causing postharvest decay in Orah and its influence on the microbial diversity of Orah

Abstract Objectives Isolate biocontrol microorganisms that inhibit Penicillium crutosum, and explore their effects on the microbial control and microbial diversity change of Penicillium crustosum causing postharvest decay in Orah. Materials and Methods The biocontrol effect was verified by confrontation experiment. The microbial diversity was analyzed using high-throughput sequencing technology. Metabolomics analysis was performed using LC-MS/MS. Results a strain of Meyerozyma guilliermondii PJ15 that strongly inhibited Penicillium crustosum was isolated from grape surface, which could effectively inhibit the mycelium growth and green spore production of Penicillium crustosum. PJ15 could reduce the sourness and nutrient loss of Orah caused by Penicillium crutosum. Compared with the control, PJ15 treatment increased pH by 11.32%, soluble protein by 154.51%, ascorbic acid by 160.99%, and soluble total sugar by 203.53%. Microbial diversity analysis showed that PJ15 has a relatively small impact on the bacterial composition and diversity on the surface of Orah, but has a significant impact on the fungal composition and diversity. It can increase the fungal diversity of Orah invaded by Penicillium crustosum, increase the density of fungal interaction networks, and form a stronger coexisting survival interaction network. The vaccination of PJ15 downregulated differential metabolites 5,6-Epoxytetraene, which showed a positive correlation with Penicillium crustosum and a negative correlation with PJ15. Conclusions Meyerozyma guilliermondii PJ15 has a strong inhibitory effect on the growth of Penicillium crustosum, and has a significant impact on the fungal composition and diversity on the surface of Orah. It has the potential for biocontrol of Penicillium crustosum causing postharvest decay in Orah.

Characteristics and Comparative Analysis of Six Mitogenomes of Genus Kiefferulus Goetghebuer, 1922 (Diptera: Chironomidae).

Chironomidae is a cosmopolitan and species-rich family of insects, with many species serving as useful indicators of aquatic ecosystem health. In this study, we newly sequenced six species of Kiefferulus Goetghebuer, 1922 (Chironomidae: Chironominae) by high-throughput sequencing technology. We analyzed characters of the mitochondrial genome, including the sequence length, nucleotide composition, and evolutionary rates of this genus. The size of the newly obtained sequences ranged from 15,588 to 15,767 bp, and all of them included 22 tRNAs, 13 PCGs, 2 rRNAs, and 1 CR. The CR showed the highest AT content relative to the PCGs, rRNAs, and tRNAs. Relative synonymous codon usage analysis showed that UUA, UUU, and AUU are the preferred codons. The ratio of nonsynonymous (Ka) to synonymous (Ks) substitution rates showed that all Ka/Ks of PCGs were lower than 1, with ATP8 having the highest evolution rate, while COX1 exhibited the lowest evolution rate. We reconstructed the phylogenetic relationship of the genus Kiefferulus based on eight species (six ingroups and two outgroups), using five matrices and employing Maximum likelihood and Bayesian inference approaches. Phylogenetic analysis of the Kiefferulus showed that six species within this genus were classified into a monophyletic clade.

Transcriptome analysis provides new insight into the mechanism of Bombyx mori under zinc exposure

Zinc is a significant source of heavy metal pollution that poses risks to both human health and biodiversity. Excessive concentrations of zinc can hinder the growth and development of insects and trigger cell death through oxidative damage. The midgut is the main organ affected by exposure to heavy metals. The silkworm, a prominent insect species belonging to the Lepidoptera class and widely used in China, serves as a model for studying the genetic response to heavy metal stress. In this study, high-throughput sequencing technology was employed to investigate detoxification-related genes in the midgut that are induced by zinc exposure. A total of 11,320 unigenes and 14,723 transcripts were identified, with 553 differentially expressed genes (DEGs) detected, among which 394 were up-regulated and 159 were down-regulated. The Gene Ontology (GO) analysis revealed that 452 DEGs were involved in 18 biological process subclasses, 14 cellular component subclasses and 8 molecular functional subclasses. Furthermore, the KEGG analysis demonstrated enrichment in pathways such as Protein digestion, absorption and Lysosome. Validation of the expression levels of 9 detoxification-related DEGs through qRT-PCR confirmed the accuracy of the RNA-seq results. This study not only contributes new insights into the detoxification mechanisms mechanism of silkworms against zinc contamination, but also serves as a foundation basis for understanding the molecular detoxification processes in lepidopteran insects.

Identification, genome-wide sequencing and analysis of drug resistance genes in a novel Vibrio harveyi strain isolated from yellowfin seabream

Yellowfin seabream (Acanthopagrus latus) is a fast-growing, adaptable species crucial for fish farming in southern China. However, as aquaculture of this species expands, diseases caused by bacteria, particularly Vibrio harveyi, are causing substantial economic losses. In this study, we isolated and identified a Vibrio harveyi strain denoted VH-AQ-SCAU-GZ23 from the diseased fish. Clinical signs of VH-AQ-SCAU-GZ23 infection were slow swimming, loss of appetite, loss of scales, severe liver congestion, and swelling of the gill filaments. VH-AQ-SCAU-GZ23 also induced an inflammatory response in yellowfin snapper spleen, liver, and brain tissue. To examine its pathogenicity and antibiotic susceptibility, we analyzed the genome sequence of this strain with third-generation high-throughput technology. The VH-AQ-SCAU-GZ23 genome was found to comprise a circular chromosome and three plasmids. Virulence factor analysis indicated that VH-AQ-SCAU-GZ23 contains a TLH gene encoding a thermolysin that increases virulence. Additionally, several antibiotic resistance genes were identified, including those encoding β-lactams, tetracyclines, aminoglycosides, quinolones, macrolides, peptides, and other antibiotics. To assess the drug sensitivity of those antibiotic resistance genes, we cloned ten resistance genes and transfected them into Escherichia coli DH5α for drug susceptibility testing. These resistance genes enhanced the resistance of DH5α to streptomycin, ampicillin, and penicillin G, among other antibiotics. Our study provides crucial support and a theoretical basis for advancing understanding of Vibrio harveyi. The insights gained are expected to provide guidance for future endeavors aimed at preventing and managing marine bacterial infections.

Preparation of nitrocellulose microspheres based on low-cost high-throughput microfluidic technology

Preparation of nitrocellulose microspheres based on low-cost high-throughput microfluidic technology

In-silico identification of putatively functional intergenic small open reading frames in the cucumber genome and their predicted response to biotic and abiotic stresses.

The availability of high-throughput sequencing technologies increased our understanding of different genomes. However, the genomes of all living organisms still have many unidentified coding sequences. The increased number of missing small open reading frames (sORFs) is due to the length threshold used in most gene identification tools, which is true in the genic and, more importantly and surprisingly, in the intergenic regions. Scanning the cucumber genome intergenic regions revealed 420 723 sORF. We excluded 3850 sORF with similarities to annotated cucumber proteins. To propose the functionality of the remaining 416 873 sORF, we calculated their codon adaptation index (CAI). We found 398 937 novel sORF (nsORF) with CAI ≥ 0.7 that were further used for downstream analysis. Searching against the Rfam database revealed 109 nsORFs similar to multiple RNA families. Using SignalP-5.0 and NLS, identified 11 592 signal peptides. Five predicted proteins interacting with Meloidogyne incognita and Powdery mildew proteins were selected using published transcriptome data of host-pathogen interactions. Gene ontology enrichment interpreted the function of those proteins, illustrating that nsORFs' expression could contribute to the cucumber's response to biotic and abiotic stresses. This research highlights the importance of previously overlooked nsORFs in the cucumber genome and provides novel insights into their potential functions.

Analysis of bacterial community structure, functional variation, and assembly mechanisms in multi-media habitats of lakes during the frozen period

Ice, water, and sediment represent three interconnected habitats in lake ecosystems, and bacteria are crucial for maintaining ecosystem equilibrium and elemental cycling across these habitats. However, the differential characteristics and driving mechanisms of bacterial community structures in the ice, water, and sediments of seasonally frozen lakes remain unclear. In this study, high-throughput sequencing technology was used to analyze and compare the structure, function, network characteristics, and assembly mechanisms of bacterial communities in the ice, water, and sediment of Wuliangsuhai, a typical cold region in Inner Mongolia. The results showed that the bacterial communities in the ice and water phases had similar diversity and composition, with Proteobacteria, Bacteroidota, Actinobacteria, Campilobacterota, and Cyanobacteria as dominant phyla. The bacterial communities in sediments displayed significant differences from ice and water, with Chloroflexi, Proteobacteria, Firmicutes, Desulfobacterota, and Acidobacteriota being the dominant phyla. Notably, the bacterial communities in water exhibited higher spatial variability in their distribution than those in ice and sediment. This study also revealed that during the frozen period, the bacterial community species in the ice, water, and sediment media were dominated by cooperative relationships. Community assembly was primarily influenced by stochastic processes, with dispersal limitation and drift identified as the two most significant factors within this process. However, heterogeneous selection also played a significant role in the community composition. Furthermore, functions related to nitrogen, phosphorus, sulfur, carbon, and hydrogen cycling vary among bacterial communities in ice, water, and sediment. These findings elucidate the intrinsic mechanisms driving variability in bacterial community structure and changes in water quality across different media phases (ice, water, and sediment) in cold-zone lakes during the freezing period, offering new insights for water environmental protection and ecological restoration efforts in such environments.

The influence of soil salinization, induced by the backwater effect of the Yellow River, on microbial community dynamics and ecosystem functioning in arid regions

Irrigation practices and groundwater levels are critical factors contributing to soil salinization in arid and semi-arid regions. However, the impact of soil salinization resulting from Yellow River water irrigation and recharge on microbial communities and their functions in the Huinong District has not been thoroughly documented. In this study, high-throughput sequencing technology was employed to analyze the diversity, composition, and structure of bacterial and fungal communities across a gradient of salinized soils. The results indicated that the alpha diversity of bacterial communities was significantly higher in slightly saline soils compared to highly saline soils. Soil salinization notably influenced the composition of both bacterial and fungal communities. Highly salinized soils were enriched with bacterial taxa such as Halomonas, Salinimicrobium, Pseudomonas, Solibacillus, and Kocuria, as well as fungal taxa including Emericellopsis, Alternaria, and Podospora. In these highly saline soils, bacterial taxa associated with iron respiration, sulfur respiration, and hydrocarbon degradation were more prevalent, whereas fungal taxa linked to functions such as soil animal pathogens, arbuscular mycorrhizal symbiosis, endophytes, dung saprotrophy, leaf saprotrophy, soil saprotrophy, fungal parasitism, and plant pathogenicity were less abundant. Random forest analysis identified nine bacterial and eighteen fungal taxa as potential biomarkers for salinity discrimination in saline soils. Symbiotic network analysis further revealed that soil salinization pressure reduced the overall complexity and stability of bacterial and fungal communities. Additionally, bacterial community assembly showed a tendency shift from stochastic to deterministic processes in response to increasing salinity, while fungal community assembly remained dominated by deterministic processes. provide robust evidence that soil salinity is a major inhibitor of soil biogeochemical processes in the Huinong District and plays a critical role in shaping bacterial and fungal communities, their symbiotic networks, and their assembly processes.

Impact of Transgenic Maize Ruifeng125 on Diversity and Dynamics of Bacterial Community in Rhizosphere Soil.

With the development of commercialized planting of genetically modified crops, their ecological security risks remain a key topic of public concern. Insect-resistant genetically modified maize, Ruifeng125, which expresses a fusion Bt protein (Cry1Ab-Cry2Aj), has obtained the application safety certificate issued by the Chinese government. To determine the effects of Ruifeng125 on the diversity and dynamics of bacterial communities, the accumulation and degradation pattern of the fusion Bt protein in the rhizosphere soil of transgenic maize were detected. Results showed that the contents of Bt protein varied significantly at different developmental stages, but after straw was returned to the field, over 97% of Bt proteins were degraded quickly at the early stages (≤10 d) and then they were degraded at a relatively slow rate. In addition, the variations in bacterial community diversity in the rhizosphere soil were detected by 16S ribosomal RNA (Rrna) high-throughput sequencing technology. A total of 44 phyla, 435 families, and 842 genera were obtained by 16S rRNA sequencing, among which Proteobacteria, Actinobacia, Acidobacter Acidobacterium, and Chloroflexi were the dominant taxa. At the same developmental stage, no significant differences in soil bacterial diversity were detected between Ruifeng125 and its non-transgenic control variety. Further analysis revealed that developmental stage, rather than the transgenic event, made the greatest contribution to the changes in soil microbial diversity. This research provides important information for evaluating the impacts of Bt crops on the soil microbiome and establishes a theoretical foundation for their environmental safety assessment.