Microbial community characterization in semi-hydroponic systems of Starbor kale (Brassica oleracea L.) grown under normal gravity and simulated microgravity.

Microbial community characterization in semi-hydroponic systems of Starbor kale (Brassica oleracea L.) grown under normal gravity and simulated microgravity - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Official websites use .gov A .gov website belongs to an official government organization in the United States. Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites. Search Log in Dashboard Publications Account settings Log out Search… Search NCBI Primary site navigation Search Logged in as: Dashboard Publications Account settings Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide PERMALINK Copy As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Curr Res Microb Sci . 2026 Apr 1;10:100592. doi: 10.1016/j.crmicr.2026.100592 Search in PMC Search in PubMed View in NLM Catalog Add to search Microbial community characterization in semi-hydroponic systems of Starbor kale ( Brassica oleracea L.) grown under normal gravity and simulated microgravity Labode Hospice Stevenson Naitchede Labode Hospice Stevenson Naitchede a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by Labode Hospice Stevenson Naitchede a , Onyinye C Ihearahu Onyinye C Ihearahu a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by Onyinye C Ihearahu a , Kishan Saha Kishan Saha a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by Kishan Saha a , David O Igwe David O Igwe a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by David O Igwe a , Jie Yan Jie Yan b Department of Computer Science, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by Jie Yan b , Anne A Osano Anne A Osano a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by Anne A Osano a , Supriyo Ray Supriyo Ray a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by Supriyo Ray a, ⁎ , George Ude George Ude a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA Find articles by George Ude a, ⁎ Author information Article notes Copyright and License information a Department of Natural Sciences, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA b Department of Computer Science, Bowie State University, 14000 Jericho Park Road, Bowie, MD 20715, USA ⁎ Corresponding authors. [email protected] [email protected] Collection date 2026. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). PMC Copyright notice PMCID: PMC13089075  PMID: 42005541 Highlights • Bacteria were more abundant in coco coir, particularly in stationary clinostats. • Pseudomonadota and Actinomycetota phyla were abundant under simulated gravity. • Biomarkers were highest in horizontal clinostats' coco coir under simulated gravity. • The GT2 and GT4 classes of glycosyl transferases were abundant in coco-coir samples. • The top four antibiotic resistance genes were adeF, vanY, vanT , and qacG . Keywords: Brassica oleracea , Microbial community, Normal gravity, Semi-hydroponic Simulated gravity, Metagenomic sequencing Abstract Kale is a member of the Brassicaceae family and contains a range of beneficial compounds. Given the global context of climate change, various vegetable production systems using advanced technologies, such as hydroponics, are being explored to alleviate food insecurity. Herein, we characterized the comprehensive microbial community associated with Starbor kale cultivation systems under normal gravity and simulated microgravity in coco coir, representing an innovative approach compared to previous studies. The kale seedlings were planted in growth vessels set into custom 2D clinostats and placed in a CONVIRON growth chamber for 43 days. The microbial DNA from coco-coir and root samples of grown kale was extracted and subjected to shotgun metagenomic sequencing. Comparisons between components revealed a higher abundance of bacteria in the soilless, while the kale roots were dominated by Eukaryota and a rchaea . The phyla Pseudomonadota and Actinomycetota were highly prevalent across all samples, with relatively high abundance in the coco coir samples from horizontal clinostats (HCR) under simulated gravity and from rotating vertical clinostats (VCR). The HCR group was associated with the highest number of biomarkers (28). Both CAZymes, glycoside hydrolases and carbohydrate esterases, exhibited higher relative abundances in the coco coir samples under normal gravity, whereas carbohydrate-binding modules were more abundant in HCR and VCR. The root samples showed much higher abundances of polysaccharide lyases (ranging from 0.00088 to 0.00097) and carbohydrate esterases (ranging from 0.030 to 0.033). The top four prevalent antibiotic resistance genes were adeF, vanY, vanT , and qacG . The findings of this investigation are crucial for the cultivation of kale and leafy green agriculture in hydroponic systems. Graphical abstract Open in a new tab 1. Introduction Plants host specific groups of microorganisms in their phyllosphere and rhizosphere, forming unique microbial communities. While interactions between plants and individual beneficial or harmful microbes are well-known, understanding the overall relationship between plants and their entire microbiome remains less clear. The microbiome refers to the diverse community of microorganisms, including bacteria, fungi, archaea, and viruses, that live in and around plants ( Leonard and Toro, 2023 ; Gallardo-Navarro et al., 2024 ; Toor et al., 2024 ). Recent advances in sequencing technology have increased our understanding of microorganisms, which are critical to human and plant health. In future precision agriculture, beneficial bacteria may help control environmental and operational challenges to preserve crop yields ( Getahun et al., 2024 ; Rodriguez et al., 2019 ). It's a dynamic and complex system, with the plant actively shaping and being shaped by its microbiome ( Trivedi et al., 2021 ). These microorganisms regulate a wide range of metabolic functions and, therefore, play a crucial role in determining health outcomes and disease progression. The plant microbiome acts like a second genome, enhancing a plant's nutrition, resistance to disease, and ability to withstand abiotic stress ( Afridi et al., 2022 ; Pascale et al., 2020 ; Huws et al., 2018 ). Hence, the host plant and its microbiota are interconnected, making a plant a holobiont ( Poupin et al., 2023 ; Glick and Gamalero, 2021 ). For instance, abiotic stresses, such as extreme environmental conditions, significantly impact agricultural productivity by affecting plant growth and development. Microorganisms can produce substances that help plants manage these stresses, and the interaction between plants and microbes often creates a mutually beneficial ecosystem ( Desai et al., 2024 ; Munir et al., 2022 ). However, climate change-induced alterations in microbial community composition may influence host plant function. Therefore, the study of microbiomes and their interactions with various environments remains of great importance in the biological sciences today. Kale ( Brassica oleracea L.; 2n= 18) is a member of the Brassicaceae family, which has a very complex taxonomy and systematics that currently includes 341 genera and 3977 species, varying over time as new species are discovered and modern techniques are applied to refine the understanding of genetic diversity within the family ( Al-Shehbaz, 2025 ; German et al., 2023 ; Raza et al., 2020 ; Hloušková et al., 2019 ). Brassica oleracea is a leafy green vegetable that was first cultivated in the Mediterranean and Asia Minor regions about 2000 years ago ( Priyadarshan and Jain, 2022 ; Mabry et al., 2021 ). This crop is produced annually and harvested over 2 months, depending on variety and growing conditions. Kale has been recognized as a food crop since around 2000 B.C., supported by a report from Theophrastus in 350 B.C. that described its characteristics ( Satheesh and Workneh Fanta, 2020 ). It is part of the growing demand for organic products in the world, with U.S. organic food sales rising significantly from $3.6 billion in 1997 to $52.5 billion in 2018 ( Reda et al., 2021 ). In the past decade, there has been increasing interest in consuming antioxidant-rich vegetables, with kale recognized as a particularly good source of antioxidants compared to other vegetables ( Łukaszyk et al., 2025 ). In addition, kale contains a range of beneficial compounds, including proteins, vitamins, and minerals, which offer various health benefits such as antibacterial, anti-cancer, antioxidant, and anti-inflammatory properties ( Galanty et al., 2025 ; Khalid et al., 2023 ). Moreover, kale is shown to have other benefits when included in the diet, but the reasons for its superiority over other cruciferous vegetables are unclear ( Šamec et al., 2019 ) Although plant-soil microbial symbiosis is much more advanced and widespread than originally thought, the composition of microbial communities and the interaction between them are yet to be adequately characterized. Studies have shown that microgravity stressors limit the growth of certain microbiota in plants and thereby influence their nutrient absorption, metabolic, and other physiological changes ( Cui et al., 2023 ). On the other hand, it would be interesting to see how microgravity will influence the growth of the microbiome in kale. Gravitational effects can be minimized by using a free-fall orbit, thereby balancing gravity's centripetal force with centrifugal force. This situation is often called microgravity. Additional methods, like clinostats and magnetic levitation, are also used to negate gravity's impact on plant growth, allowing researchers to study plant responses to stimuli ( Kiss et al., 2019 ). The rationale for using clinostats in plants is to highlight how rotation rate affects their effectiveness, particularly in influencing the movement of starch statoliths, which are believed to play a key role in gravity detection ( Galanty et al., 2025 ). The knowledge of the composition of microorganism populations in plant ecosystems under clinostat-influenced microgravity will enhance understanding of relationships and functions within the microbiome, which, in turn, will allow for the establishment of sustainable and healthy soil-plant systems for food production under microgravity. Given that there are no root systems in nature without a microbial community, the functional biodiversity of soil organisms can be associated with the maintenance of soil functional activities ( Pedrinho et al., 2024 ; Delgado-Baquerizo et al., 2020 ; Maron et al., 2018 ). Indeed, they interact with the soil-plant ecosystem to meet plants' nutritional needs by dissolving minerals, storing energy, maintaining soil microbial and ecological balance, increasing photosynthetic efficiency, and fixing biological nitrogen ( Xing et al., 2025 ; Fahad et al., 2024 ). Numerous plant growth-promoting rhizobacteria have been used as soil inoculants aimed at helping the solubilization of mineral phosphates and other nutrients, boosting stress resistance, stabilizing soil aggregates, and enhancing soil structure and organic matter levels ( Hasan et al., 2024 ; Basu et al., 2021 ; Sammauria et al., 2020 ; Alori et al., 2017 ). Studies indicated that free-living soil bacteria like the genera Rhizobium, Bradyrhizobium, Azorhizobium, Allorhizobium, Sinorhizobium, and Mesorhizobium promote the production of plant nutrients, including hormones such as auxins, cytokinins, gibberellins, ethylene, and abscisic acid ( Sangeetha et al., 2026 ; Jehani et al., 2023 ). Some bacterial strains from the genera Pseudomonas, Bacillus, Rhizobium, Burkholderia, Achromobacter, Agrobacterium, Micrococcus, Aerobacter, Flavobacterium , and Erwinia can solubilize insoluble inorganic phosphate compounds, including tricalcium phosphate, dicalcium phosphate, hydroxyl apatite, and rock phosphate ( Rawat et al., 2021 ). Certain bacteria, such as Azospirillum, Azotobacter, Bacillus , and Pseudomonas , are also available for various crops and can be used individually or in combination with Rhizobium sp. Nonetheless, the structure of the rhizosphere bacterial community is influenced by soil type and fertilizer application ( Bo et al., 2023 ; Hummerick et al., 2021 ). Space microgravity prevents water distribution through the root modules of plant development gear and reduces gas exchange due to the absence of convective mixing and buoyancy ( De Pascale et al., 2021 ). Instead of trying to replicate Earth-like conditions, cultivation technology could leverage the unique characteristics of spaceflight to develop innovative solutions. Due to these limitations and to reduce the single-use mass required to produce a comparable quantity of biomass, hydroponics-like growing is crucial. This technology eliminates the need for soil and reduces water consumption by delivering nutrients directly to the plant's roots ( Rajaseger et al., 2023 ; Velazquez-Gonzalez et al., 2022 ). It has been proven effective in low gravity environments such as spacecraft microgravity, the lunar, and Mars ( Hasenstein and Miklave, 2024 ). Using nutrient-poor coco coir as a porous substrate and regular watering techniques can help shift to low-gravity systems. Furthermore, coco coir is an ideal soilless medium for promoting healthy, efficient plant growth in hydroponic systems, as it retains a significant amount of water, oxygenates the roots, and maintains a neutral pH for optimal nutrient absorption ( Hosamani et al., 2024 ). Future space missions may incorporate crop production systems to provide fresh food for crew members. This can also be done using similar methods to foster plant growth under microgravity for food production, such as the vegetable production system and clinorotation, helping to address the challenges of supplying space stations and long-term missions ( Kumar et al., 2023 ; Ehrlich et al., 2017 ). Space agriculture may improve farming on Earth by increasing yields, enhancing production efficiency, and utilizing closed-loop systems for recycling fertilizers and water ( Fu et al., 2013 ). Although it has been evidenced that plants grow in space under microgravity conditions and can even complete their life cycle from seed to seed, microgravity and ionizing radiation can still impact their physiological processes. To ensure the health and viability of these crops and their associated microbiomes, it is important to understand plant growth systems tailored for the challenges of space. This includes how plants respond to Earth’s environmental factors, as well as unique space conditions like radiation, limited space, and changed gravity ( Maffei et al., 2024 ; De Micco et al., 2023 ; Prasad et al., 2021 ). Recent advancements in molecular biology, such as omics approaches, particularly metagenomics, are helping to characterize the complex relationships between plants and their microbial communities, which are vital for growth and health ( Sharma et al., 2020 ; Kumar et al., 2021 ; Wheeler, 2017 ). This study aimed to identify the microbial communities, particularly beneficial microorganisms, in kale-grown systems and investigate how their distribution differs under hydroponic and simulated microgravity conditions. The resulting outcome will enhance space atmospheric agricultural technologies and growth recipes to maximize fresh food production within space and energy constraints. 2. Materials and methods 2.1. Crop material and treatment The Starbor kale (Brassica oleracea var. Starbor ) seeds used in this study were collected from West Coast Seeds Ltd. (Delta, BC, V4L 2P1, Vancouver, British Columbia, Canada). Fifty seeds were germinated using rockwool growing media (4 cm x 3.5 cm x 1 cm; Crain Hwy, Bowie, MD, USA) in a tray (48 cm x 32 cm x 12 cm; Crain Hwy, Bowie, MD, USA) filled to a quarter with tap water over a period of three weeks. Thereafter, 24 seedlings were transferred to 24 BioWorld GA-7 “Magenta” growth vessels (10 cm x 6 cm; Carolina Biological Supply Company, Burlington, NC, USA) filled with a 3:1 (v/v) mixture of coco coir and perlite (soilless medium; HTG Supply, Callery, PA, USA). The substrate was used as an uncharged inert medium and was pre‑buffered to a neutral pH (6.3–6.8). A closed semi-hydroponic system was applied to directly supply nutrients to the plants ( Fig. 1 ). Briefly, a flexible PVC transparent plastic tube aquarium hose (15 cm, 10 × 12 mm; Crain Hwy, Bowie, MD, USA) was placed inside each pot, extending all the way to the base, while the opposite end remained outside to facilitate the delivery of a nutrient solution (20 mL) to the plants three times a week. The nutrient solution consisted of macronutrients and micronutrients ( Table 1 ). The growth vessels containing the seedlings were set into custom COSE clinostats (Bowie State University, MD, USA) and placed in the CONVIRON growth chamber (Model No. ATC26, Serial No. 150422, Winnipeg, MB, Canada) at 23°C ± 2 and 45 µmol m −2 s −1 light intensity with a 16 h photoperiod at Bowie State University in the Department of Natural Sciences, USA, as described by ( Naitchede et al., 2025 ). Fig. 1. Open in a new tab Closed semi-hydroponic system. (A) Empty “Magenta” growth vessels with flexible PVC transparent plastic tube aquarium hose. (B) Starbor kale seedling in “Magenta” growth vessels with flexible PVC transparent plastic tube aquarium hose. Table 1. Nutrient composition of hydroponic fertilizers in 1 gallon of distilled water. Macro-nutrient Concentration (%) Micro-nutrient Concentration (mg/L) Nitrate-Nitrogen NO 3 — N 14.5 Boron B 200 Ammonium-Nitrogen NH 4 — N 1 Copper Cu 105 Phosphoric acid H₃PO₄ 9.3 Iron Fe 2100 Potassium oxide K 2 O 35 Manganese Mn 190 Magnesium Mg 39 Molybdenum Mo 42 Calcium Ca 19 Zinc Zn 210 Open in a new tab 2.2. Simulation of microgravity Experiments were conducted using 2D clinostats placed in the CONVIRON growth chamber ( Fig. 2 ). The clinostats were designed with a mechanism that allowed samples to rotate around a single axis, either perpendicular (horizontally) to the gravitational pull to mitigate its influence (simulated gravity) or vertically aligned with the gravitational field (rotating 1 g control). Simulated microgravity and rotating 1 g control conditions were obtained using clinostats set to 10 rpm, as described in various studies ( Poon, 2020 ; Ulbrich et al., 2014 ). At this rotation speed, the gravity vector's orientation relative to the sample changes continuously and rapidly, preventing cells from perceiving a stable downward direction. The 10‑rpm frequency is typically recommended for functional microgravity simulation due to its ability to suppress gravity-dependent biological responses while minimizing centrifugal forces. Under these conditions, the gravitational vector is effectively averaged to near zero over time, allowing the biological system to experience a quasi‑weightless environment comparable to that observed during spaceflight. Additionally, clinostats were engineered to achieve instantaneous fixation during clinorotation, preventing the device from stopping. The 2D-clinostat rotation was promptly initiated, with all samples subjected to the same environmental conditions. Twenty-four (24) clinostats were used: − 12 clinostats set in rotation with 6 biological replicates in 6 horizontal clinostats, and 6 biological replicates in 6 vertical clinostats. − 12 clinostats were set stationary as controls, with 6 biological replicates in 6 horizontal clinostats, and 6 biological replicates in 6 vertical clinostats. Fig. 2. Open in a new tab Experimental setup showing the clinorotation systems used to generate simulated microgravity (A) and the rotating 1 g control (B) for kale cultivation within the CONVIRON growth chamber at the Department of Natural Sciences, Bowie State University, USA. 2.3. Coco coir potting mix and root sampling Plants were grown for 43 days following transplantation ( Fig. 3 ). Roots and coco coir potting mix were collected as described by Simmons et al. (2018) . Briefly, before sampling soilless medium (coco coir) and root samples, ultrapure water was autoclaved, and an epiphyte removal buffer was prepared by dissolving 6.75 g of KH 2 PO4, 8.75 g of K 2 HPO4, and 1 mL of Triton X-100 (Sigma‑Aldrich Corporation; Milwaukee, WI, USA) in 1 L of sterile water. The buffer was sterilized using a vacuum filter with a 0.2 µm pore size. Samples of coco coir potting mix were collected at a depth of 5 cm using an ethanol-sterilized core collector to prevent contamination from plant roots. For root samples, the potting mix and plantlets were carefully removed together, with excess mix shaken off, leaving roughly 2 mm on the roots. A section of the roots was then cut using sterilized scissors. To remove the rhizosphere, root samples were subjected to ultrasound at 4°C for 10 min, using 160 W pulses with 30 s intervals. Following this, the roots were carefully placed into a pre-chilled 50 mL Falcon tube using sterile forceps and rinsed with 20 mL of chilled sterile water. The roots were cleaned thoroughly by shaking them vigorously by hand for 15 to 30 s, followed by draining the water. This washing process was repeated twice until the root surfaces were free of any soil. Coco coir potting mix and root samples were transferred to separate 50 ml Falcon tubes, placed on dry ice, and stored at -80°C until DNA extraction. Fig. 3. Open in a new tab 43-day-old Starbor kale plantlets collected from CONVIRON growth chamber. (A) H1 to H6: horizontal clinostats containing plantlets under stationary condition; (B) RH1 to RH6: rooted plantlets from horizontal clinostats in stationary condition; (C) V1 to V6: vertical clinostats containing plantlets under stationary condition; (D) RV1 to RV6: rooted plantlets from vertical clinostats under stationary condition; (E) H1 to H6: horizontal clinostats containing plantlets under clinorotation condition; (F) RH1 to RH6: rooted plantlets from horizontal clinostats under clinorotation condition; (G) V1 to V6: vertical clinostats containing plantlets under clinorotation condition; (H) RV1 to RV6: rooted plantlets from vertical clinostats under clinorotation condition. Scale bar= 7 cm. 2.4. DNA extraction DNA extraction was performed on the 24 kale root samples and the 24 coco coir samples, for a total of 48 samples. The microbial genomic DNA was extracted from the coco coir and root‑associated samples using the DNeasy PowerSoil Pro Kit (Qiagen, USA) following the manufacturer’s instructions, with some modifications. Briefly, liquid nitrogen was employed to freeze and grind root tissues. The extract was prepared from 250 mg of homogenized material, which was added to the PowerBead Pro tube, and cells were lysed by mechanical disruption on a bead‑beating device. Lysates were clarified and passed through a silica‑based spin column to selectively bind DNA. The Inhibitor Removal Technology (IRT) included in the kit was used to eliminate contaminating organic and inorganic materials that could compromise DNA purity and hinder subsequent DNA processes. In the presence of a high-salt solution, DNA was bound to the silica membrane within the MB spin column. After sequential wash steps to remove inhibitors present in soil substrates, purified DNA was eluted in 35 µL of nucleic acid-free water. DNA concentration and purity were assessed using a NanoDrop spectrophotometer prior to downstream library preparation. Qualitative analysis of genomic DNA in 1% agarose and bulk PCR amplification of 16S rRNA were performed to confirm the presence of microbial DNA in the samples (Fig. S1–S6). Thereafter, DNA samples from each biological replicate setup were pooled into a single normal 3-biological-replicate pool, yielding a total of 24 samples for effective analysis. 2.5. Library construction, quality control, and shotgun metagenomic sequencing A total of 24 biological replicates were generated from the 48 samples ( Table 2 , Table 3 ) and subjected to shotgun metagenomic sequencing. Sequencing was performed on an Illumina HiSeq platform at Novogene (Sacramento, CA 95817, USA). Briefly, 1 μg of genomic DNA was sampled and randomly fragmented into 350 bp segments using a Covaris ultrasonic disruptor to construct the library. The entire library preparation process was completed through a series of steps, including end repair, addition of A-tails, ligation of sequencing adapters, purification, and PCR amplification. After library construction, the integrity of the library fragments and the size of the inserted fragments were assessed using AATI analysis. The accurate concentration of the effective library was quantified using quantitative PCR (effective library concentration > 3 nM) to ensure library quality. After the library passed the quality check, libraries were pooled based on their effective concentrations and target data output requirements, then subjected to PE150 sequencing. Table 2. Biological replicates (Bio rep) in DNA from stationary clinostat samples. Samples/ Clinostat orientation Coco coir samples from horizontal clinostats (HC) Coco coir samples from vertical clinostats (VC) Root samples from horizontal clinostats (HCroot) Root samples from vertical clinostats (VCroot) Combination Bio rep code H1+H2 G1 H3+H4 G2 H5+H6 G3 V1+V2 G4 V3+V4 G5 V5+V6 G6 RH1+RH2 G7 RH3+RH4 G8 RH5+RH6 G9 RV1+RV2 G10 RV3+RV4 G11 RV5+RV6 G12 Open in a new tab Table 3. Biological replicates (Bio rep) in DNA from samples under simulated gravity and a rotating 1 g control. Samples/ Clinostat orientation Coco coir samples from horizontal clinostats (HCR) Coco coir samples from vertical clinostats (VCR) Root samples from horizontal clinostats (HCRroot) Root samples from vertical clinostats (VCRroot) Combination Bio rep code H1+H2 R1 H3+H4 R2 H5+H6 R3 V1+V2 R4 V3+V4 R5 V5+V6 R6 RH1+RH2 R7 RH3+RH4 R8 RH5+RH6 R9 RV1+RV2 R10 RV3+RV4 R11 RV5+RV6 R12 Open in a new tab 2.6. Bioinformatics and statistical analysis pipeline 2.6.1. Preprocessing of sequencing data and metagenome assembly The bioinformatics section of this work, including preprocessing of sequencing results and metagenome assembly, was performed as described by Ohlsson et al. (2025) . Briefly, the Illumina sequencing platform's raw data, including raw read counts and assembly statistics (Tables S1 and S2), were cleaned for subsequent analysis using Fastp ( https://github.com/OpenGene/fastp ). The host database was queried for clean data to remove any potential host-derived reads that could have contaminated the samples. The Bowtie2 software ( http://bowtie-bio.sourceforge.net/bowtie2/nsdex.shtml ) was used with the following parameters: –end-to-end, –sensitive, -I 200, and -X 400 ( Karlsson et al., 2013 , 2012 ). Clean data assembly analysis was performed with MEGAHIT using the following assembly parameter settings: –presets meta-large (–end-to-end, –sensitive, 200, 400) ( Nielsen et al., 2014 ) while scaftigs devoid of N were generated from breaking the resulting scaffolds at the N junction ( Li et al., 2015 ; Kultima et al., 2012 ). 2.6.2. Gene prediction and abundance analysis MetaGeneMark ( http://topaz.gatech.edu/GeneMark/ ) was executed to predict ORFs for scaftigs (≥ 500 bp) in each sample using default parameters ( Karlsson et al., 2012 ; Mende et al., 2012 ; Qin et al., 2014 ). Afterwards, data shorter than 100 nt was filtered out ( Zhou et al., 2010 ; Zhu et al., 2010 ; Zeller et al., 2014 ; Sunagawa et al., 2015 ). With parameter settings of -c 0.95,-G 0,-aS 0.9,-g 1,-d 0, CD-HIT software ( http://www.bioinformatics.org/cd-hit/ ) was used to reduce duplication ( Fu et al., 2012 ) and get the non-redundant starting gene catalogue (Successive non-redundant genes encoded nucleic acid sequences termed genes) ( Li et al., 2014 ). Bowtie2 was used to count gene reads on each sample alignment using the following parameters: –end-to-end, –sensitive, -I 200, -x 400 ( Qin J et al., 2010 ). By selecting genes with reads ≤ 2 in each sample, we created the final gene catalogue (Unigenes) for analysis. The abundance of each gene in each sample was determined by the number of reads aligned and its length. Basic statistics, core-pan gene analysis, correlation analysis between samples, and Venn diagram gene number analysis were performed based on gene abundance in each sample ( Villar et al., 2015 ; Buchfink et al., 2015 ; Cotillard et al., 2013 ). Utilizing the abundance tables, analyses including clustering, Anosim, PCA, PCoA, and NMDS were conducted. For grouped data, MetaGenomeSeq and LEfSe were used for multivariate analysis and pathway comparison. 2.6.3. Species annotation Unigenes were aligned with the Micro_NR database of bacteria, fungi, archaea, and viruses using DIAMOND software ( https://github.com/bbuchfink/diamond/ ) with a parameter setting of 1e-5 of the blastp algorithm ( Karlsson et al., 2013 ). Among the alignment findings, the sequence with an e-value ≤ 10 was chosen. For species annotation, the LCA algorithm (applied to systematic taxonomy of MEGAN software ( https://en.wikipedia.org/wiki/Lowest_common_ancestor )) was used since each sequence could have numerous alignment results ( Huson et al., 2011 ). The abundance of each sample at each taxonomy (kingdom, phylum, class, order, family, genus, or species) and gene abundance tables were obtained from the LCA annotation. The abundance of a species equaled the sum of its species-annotated genes ( Feng et al., 2015 ; Karlsson et al., 2012 ). The number of species genes in a sample equaled the number of species genes with non-zero abundance. Based on abundance tables at each taxonomic level, a Krona analysis, a relative abundance overview, and an abundance clustering heatmap were generated ( Ondov et al., 2011 ). Dimension reduction was achieved with principal component analysis (PCA) and non-metric multidimensional scaling (NMDS). Group differences were tested using Anosim. Group species differences were identified using MetaGenomeSeq and LEfSe. A p-value was obtained from a MetaGenomeSeq permutation test comparing taxonomic groups, and the analysis was done using LEfSe (LDA Score was 4 by default) ( Segata et al., 2011 ). Gradient-based Random Forest analysis ( Weng et al., 2025 ) was used to select species and develop a Random Forest model. Each model underwent cross-validation (default: 10-fold) after identifying important species using MeanDecreaseAccuracy and MeanDecreaseGini. This was followed by the drawing of the ROC curves. 2.6.4. Annotations of the common functional database The following parameter settings were used to align unigenes with the functional database using DIAMOND ( https://github.com/bbuchfink/diamond/ ): blastp -e 1e-5. The functional databases encompassed KEGG ( http://www.kegg.jp/kegg/ ), eggNOG ( http://eggnogdb.embl.de/#/app/home ), CAZy ( http://www.cazy.org/ ), VFDB ( http://www.mgc.ac.cn/VFs/main.htm ), and PHI ( http://www.phi-base.org/index.jsp ) ( Kanehisa et al., 2017 ; Huerta-Cepas et al., 2016 ). After aligning each sequence, the best BLAST hits were selected. From the alignment outputs, the relative abundance at each functional level was determined as the sum of the relative abundance of genes identified as that functional level ( Bäckhed et al., 2015 ; Qin et al., 2012 ; Karlsson et al., 2012 ). From the functional annotation and gene abundance tables, a gene number table was created for each sample at each taxonomy level. The number of genes having a function in a sample equaled the number of non-zero abundance genes annotated with that function. The annotated genes statistics, relative abundance overview, abundance clustering heat map, PCA and NMDS dimension reduction analysis, Anosim functional abundance analysis, metabolic pathway comparative analysis, and MetaGenomeSeq and LEfSe functional differentiation analysis were all performed using the abundance table at each taxonomic level. 2.6.5. Annotations of the resistance genes Unigenes (Tables S3, S4, and S5) were aligned to the Comprehensive Antibiotic Resistance Database (CARD) database ( https://card.mcmaster.ca/ ) ( Martínez et al., 2015 ) using the Resistance Gene Identifier (RGI) software ( Jia et al., 2017 ) provided by the CARD database (RGI built-in Blastp, default evalue < 1e-30) ( McArthur et al., 2013 ). Based on the RGI alignment results and unigene abundance information, the relative abundance of each Antibiotic-Resistant Ontology (ARO) was calculated. Based on the abundance of ARO, the abundance histogram, abundance clustering heat map, abundance distribution circle map, ARO difference analysis between groups, resistance genes (unigenes annotated as ARO), and species attribution analysis of resistance mechanism were carried out (some AROs with long names are abbreviated as the first three words plus underlines). The mobile genetic elements (MGEs) and unigenes were compared against the insertion sequence (isfinder), integrall, and plasmid databases to obtain abundance information (Tables S6, S7, S8, and S9). The annotated abundance information was further visualized, and the abundance histogram and the relative abundance heatmap were displayed. 3. Results 3.1. Evaluation of sequencing depth, coverage completeness, and rarefaction‑based gene diversity The sequencing dataset exhibited moderate but consistent depth across all samples, with coverage values ranging from 2.26 × to 4.50 ×, appropriate for metagenomic catalog construction and aimed at recovering a broad representation of coding sequences. Most samples were clustered between 2.5 × and 3.5 ×, while R7 (4.50 ×) was the highest‑depth sample (Tables S1 and S10). To further evaluate the representativeness of the gene catalog, we performed two complementary rarefaction analyses ( Fig. 4 ). The gene accumulation rarefaction curve showed a steep initial increase in non‑redundant gene counts as early samples were added, followed by gradual tapering as additional samples contributed fewer novel genes. This asymptotic trend indicated that the sequencing effort successfully captured the majority of the population's gene diversity, with diminishing returns from additional sampling. In parallel, the gene redundancy rarefaction curve revealed a progressive decline in the number of unique genes contributed by each successive sample. Early samples contained many novel genes, but later samples exhibited substantial overlap with the existing catalog, reflecting increasing redundancy. The stabilization of this curve at higher sample numbers confirms that the gene catalog was approaching saturation, with minimal new functional information gained from additional samples. Fig. 4. Open in a new tab Gene accumulation and redundancy rarefaction curves demonstrating catalog saturation. (A) Gene accumulation rarefaction curve showing the increase in the total number of non‑redundant genes as samples are progressively added. (B) Gene redundancy rarefaction curve illustrating the number of unique genes contributed by each individual sample after redundancy removal. 3.2. Beta-diversity among different groups based on Pearson correlation matrix and principal coordinates analysis A Pearson correlation matrix was generated to assess similarity among samples based on their non‑redundant gene abundance profiles. Distinct blocks of strong positive correlations appeared within G‑group samples (G1–G12) and R‑group samples (R1–R12). Correlations between G‑group and R‑group samples were noticeably weaker ( Fig. 5 A). Fig. 5. Open in a new tab (A) Sample–sample Pearson correlation revealing clustering patterns based on gene abundance profiles. Principal coordinate analysis (PCoA) based on the Bray–Curtis distance. (B) Single samples. (C) Sample groups. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. The dissimilarity of microbial communities among the different groups was compared, and principal coordinates analysis (PCoA) based on the Bray–Curtis distance was performed. The results showed a distinct distribution of the microbiome across the eight groups ( Fig. 5 ). The first component accounted for 82.22% of the variability and was positively correlated with coco coir samples from horizontal clinostats (HC) and vertical clinostats (VC) under normal gravity, as well as coco coir samples from horizontal clinostats under simulated gravity (HCR) and those from rotating vertical clinostats (VCR). While the microbial communities in root samples from the vertical and horizontal rotating clinostats formed two distinct groups (R7–9 and R10–12), their stationary counterparts (G7–12) were clustered together ( Fig. 5 B). However, the microbial community in coco coir from the stationary condition (HC and VC) clustered together, whereas those from clinorotation (simulated microgravity and rotating 1 g control) formed two closely related groups ( Fig. 5 C). 3.3. Shared and exclusive microbial genera and microbial community characterization The microbial community characterization of the coco coir and kale roots from the different clinostats and gravity conditions was investigated at the gene composition level to determine the number of non-redundant and shared genes ( Fig. 6 ). There were a huge number of genes common to all the components (coco coir and kale roots from horizontal and vertical clinostats) and in each gravity condition (stationary and simulated microgravity or rotating vertical clinostats), ranging between 1038782 and 2432430 genes, with VC-VCR sharing the highest number of genes ( Fig. 6 G) and HC—HCR-VC-VCR-HCroot-VCroot-HCRroot-VCRroot sharing the lowest number of genes ( Fig. 6 A). When considering the normal gravity vs simulated gravity or rotating vertical clinostats, the number of shared genes is higher for VC-VCR (2432430) compared to HC—HCR (2278121 genes) ( Fig. 6 F). In the same way, VCroot-VCRroot showed higher common genes (2385302) than HCroot-HCRroot (2255797) ( Fig. 6 H & I). In the eight-vessel groups, across sample types and gravity conditions ( Fig. 6 J), the VCR and HCR recorded a high number of unique genes (2437500 and 2312500, respectively). Among the root samples, VCRroot revealed the maximum non-redundant genes, while there was a significant difference between HCroot and VCroot. Fig. 6. Open in a new tab Gene catalogue and abundance. (A, B, C, D, E, F, G, H & I) Venn Diagram of shared and unique genes across the microbial community samples. (J) Relative abundance of genes according to their sharing patterns in the Venn Diagram. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. A closer inspection of the community constituents revealed variation in microbial abundance. Among the major biological kingdoms, the coco coir exhibited a higher abundance of bacteria ( Fig. 7 A) than the root samples, with the highest value observed in the stationary clinostats (G1, G2, G3, G4, G5 & G6). These bacteria were especially Candidatus levyibacteriota, Spirochaetota, Armatimonadota, Candidatus paceibacterota, Bacteroidota, Myxococcota, and Bdellovibrionota , mainly related to the function of metabolism, cellular processes, and genetic information processing, posttranslational modification (VFC0315), stress survival, immune modulation (VFC0258), regulation, effector delivery system (VFC0086), mobility, and antimicrobial activity/competitive advantage. On the other hand, the root samples from both stationary and clinorotation conditions were found to be enriched in Eukaryota ( Olpidiomycota, Mucoromycota, Chytridiomycota , and Basidiomycota ) and archaea (Euryarchaeota) ( Figs. 7 B, 8 A & B). Fig. 7. Open in a new tab Profiles of microbiota across the samples. (A) Diversity of major microbial domains. (B) Phylum‑level composition of the microbiota. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. Fig. 8. Open in a new tab Microbial functional profiles at the general (A) and specific (B) levels. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. Six taxonomic levels of the microbial core of the grown-kale systems in coco coir were analyzed ( Fig. 9 ). At the phylum and family levels, Pseudomonadota and Actinomycetota were highly prevalent in all the samples ( Fig. 9 A) with a relatively high abundance in HCR and VCR (R-1-2-3-4-5-6). Alphaproteobacteria were highly abundant in all samples at the class level ( Fig. 9 B), while Hyphomicrobiales formed the most abundant order ( Fig. 9 C). Acidobacteriaceae, microbacteriaceae, Treboniaceae, and Nitrobacteraceae formed the core microbial families ( Fig. 9 D). The core genus mainly included Treboniaceae and Bradyrhizobium ( Fig. 9 E). The core species were formed by Trebonia kvetii , abundant in HC, HCR, VC, and VCR ( Fig. 9 F). Fig. 9. Open in a new tab Taxonomic profiling of the core microbiome in kale grown in coco coir. (A) Phylum level. (B) Class level. (C) Order level. (D) Family level. (E) Genus level. (F) Species level. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. 3.4. Phytomicrobiome community profiles analysis of grown kale systems at the phylum level Distinct differences in the microbial communities were found between the growing systems. The relative abundance of bacteria from grown kale systems in coco coir was assigned to 19 phyla ( Fig. 10 A), all of which were abundant (relative abundance > 1%), with values varying across samples. The R sample groups (Clinorotation), particularly R1-R3 (coco coir samples in the horizontal clinostats) and R7-R12 (root samples from the vertical and horizontal clinostats), clustered together and separated from the G samples (stationary clinostats). They showed strong, consistent high abundance for Pseudomonadota, Bacteroidota , and Actinomycetota . Among the ten top phyla ( Fig. 10 B), Bacteroidota, Verrucomicrobiota, Planctomycetota , and Ascomycota were the most prevalent in the coco coir of the rotating clinostats, particularly the VCR. These four phyla mostly comprised Verrucomicrobiales, Chitinophagales, Opitutales, Cytophagales, and Sordariales . However, Actinomycetota, Mucoromycota, Pseudomonadota were abundant in HC, while Gammaproteobacteria, Actinomycetota, Alphaproteobacteria , and Eurotiomycetes dominated the VC group. The root samples were enriched in Mycoplasmatota ( phytoplasma ), Pseudomonadota, Bacillota , and Bacteroidota , with higher abundance recorded in HCRroots compared to VCRroots, HCroot, and VCroots, respectively. The top seven phyla consisted of Acidobacteriota, Actinomycetota, Bacteroidota, Mycoplasmatota, Myxococcota, Pseudomonadota, Verrucomicrobiota , while the top four phyla consisted of Acidobacteriota, Actinomycetota, Bacteroidota, Pseudomonadota ( Fig. 10 C & D). However, the Acidobacteriota and Bacteroidota families were present but generally less abundant across all samples, except for the coco coir samples in stationary clinostats (G1, G2, G4, G5, and G6), compared to the peak abundances seen in the other two phyla. In contrast, the phylum Pseudomonadota and several families within Actinomycetota (e.g., Nocardioidaceae, Mycobacteriaceae, Rhizobiaceae, Acidobacteriaceae ) showed high relative abundance in certain samples, particularly samples R1, R3, R4, and R5 ( Fig. 10 D). Fig. 10. Open in a new tab Distance‑based redundancy analysis linking clinostat gravity conditions to bacterial and fungal phyla of grown kale system based on Bray-Curtis distance similarities. (A) Top nineteen phyla. (B) Top ten phyla. (C) Top seven phyla. (D) Top four phyla. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. 3.5. Functional annotation of biomarkers and carbohydrate-active enzyme classification of the microbiome community in the soilless-grown medium To better distinguish the variations in microbial species responses to clinorotation and identify potential key microbes, we conducted LEfSe (p < 0.05) analysis ( Fig. 11 A). We identified 80 biomarkers across seven phyla. Among them were five phyla of bacteria ( Planctomycetota, Bacteroidota, Actinomycetota, Verrucomicrobiota , and Pseudomonadota) , one phylum of fungi ( Ascomycota) , and one phylum of viruses ( Uroviricota) . Specifically, HCR was associated with the highest number of biomarkers (28) and unclassified classes (19), whereas HC was associated with the lowest number of biomarkers (13), with Pseudomonadota being the key biomarker phylum. However, 19 and 20 biomarkers were identified in VC and VCR, respectively, with Actinomycetota and Pseudomonadota being the abundant biomarkers, respectively. Fig. 11. Open in a new tab CAZyme‑based functional prediction of microbial communities in kale systems. (A) Cladogram. (B) Carbohydrate-active enzyme classification. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. In the CAZY classification system (carbohydrate-active enzymes), the eight sample groups were analyzed to classify the top-level 6 CAZY with respect to characterizing the vast diversity of enzymes that synthesize, modify, and degrade carbohydrates ( Fig. 11 B). Most categories showed distinct differences in abundance depending on the clinostat orientation and gravity conditions, with several statistically significant differences at p ≤ 0.05. The coco coir exhibited the highest abundance (0.034) of Glycosyl transferases (GT), especially Hexosyltransferases, 1,2-diacylglycerol 3-glucosyltransferase, N-acetylgalactosaminyltransferase, GDP-Man alpha-mannosyltransferase, alpha-L-rhamnosyltransferase, and beta-glucuronosyltransferase, with no significant difference between HC and HCR, and VC and VCR, respectively. All these glycosyltransferase (GT) classes were derived from both the GT family GT2 and GT4, except 1,2-diacylglycerol 3-glucosyltransferase and beta-glucuronosyltransferase, which belong to GT4 and GT1, respectively ( Fig. 12 A & B). The root abundances in GT ranged from 0.020 to 0.022. On the other hand, no significant differences in glycoside hydrolase abundance were observed between stationary and rotating clinostats. Other CAZyymes investigated in this study were the glycoside hydrolases (GH), which exhibited significantly higher relative abundances (0.030 to 0.033) in HC and VC, followed by HCR and VCR, compared to the root samples, which expressed lower abundances ( Fig. 11 B). This observation differed within root samples, where GH abundance was prevalent in HCR and VCR compared to their counterparts, HC and VC, respectively. However, several GH families (GH3, GH13, GH23, and GH28) exhibited higher relative abundance in the soilless-grown media, whereas specific enzyme classes associated with GH27 and GH38 (alpha‑galactosidase and beta‑L‑arabinopyranosidase) were predominantly enriched in the kale root microbiome. Similarly, the only carbohydrate esterase (CE) family recorded in this study (CE8) exhibited a significantly higher relative abundance (0.004 to 0.0042) in soilless medium samples compared to root samples, with no difference between stationary clinostats and their rotating counterparts. On the contrary, Carbohydrate-binding Modules (CBMs) were significantly more abundant in HCR than in HC, and in VCR than in VC, with the HCR group showing the highest overall values, some exceeding 0.0092. The root samples in rotating clinostats (HCRroot and VCRroot) also showed a slightly higher median CBM abundance compared to the stationary clinostat conditions (HCroot and VCroot), although overall levels were significantly lower than in soilless medium conditions. On the other hand, the root samples displayed a much higher abundance (from 0.00088 to 0.00097) of polysaccharide lyases (PL) compared to their soilless medium counterparts. However, HCRroots and VCRroots exhibited significantly higher abundance in PL than HCroots and VCroots, respectively. Similarly, a significant increase in PL abundance was observed in soilless media from stationary to rotating clinostats. These 5 CAZyme groups were found to be mostly derived from the top 3 phyla of the taxonomic classification of CAZY, such as Pseudomonadota, Actinomycetota , and Acidobacteriota ( Fig. 12 C). Fig. 12. Open in a new tab Dominant CAZyme families in the kale cultivation systems. (A & B) Top 10 Glycosyl transferases and glycoside hydrolases. (C) Top 10 phyla. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. 3.6. Profiling of antibiotic resistance genes and mobile genetic elements in the grown kale system microbiome The hierarchical clustering of samples based on their microbial communities, particularly regarding their unique antibiotic resistance profiles, revealed 2 main clusters ( Fig. 13 A). The first cluster of samples, with a diverse distribution and dominated by the taxon ' CP022915.1 ′, encompassed the soilless-grown media from both the stationary clinostats (G1-G5) and rotating clinostats (R1-R5). The second cluster, comprising the root samples from both clinostats (G7-G12 and R7-R12), was dominated by the reference genome ' AP025035.1 ′ and, to a lesser extent, by LT996886.1 . Nevertheless, an almost complete absence of the dominant taxa from the first cluster was observed. Fig. 13. Open in a new tab ARG relative abundance profiles in the kale growth systems. (A ) Reference genome‑based functional potential of microbial communities with respect to unique antibiotic resistance. ( B ) Comprehensive antibiotic resistance annotations of the kale growth systems. ( C ) Antibiotic resistance gene ontology. ( D ) Hierarchical clustering of kale system samples using the ten most common plasmids. ( E ) Proportional distribution of specific insertion sequence (IS) elements in the overall microbial community. HC (Coco coir sample from horizontal clinostats under normal gravity)= G1-3; VC (Coco coir sample from vertical clinostats under normal gravity)= G4-6; HCroot (Root sample from horizontal clinostats under normal gravity)= G7-9; VCroot (Root sample from vertical clinostats under normal gravity)= G10-12; HCR (Coco coir sample from horizontal clinostats under simulated gravity)= R1-3; VCR (Coco coir sample from rotating vertical clinostats)= R4-6; HCRroot (Root sample from horizontal clinostats under simulated gravity)= R7-9; VCRroot (Root sample from rotating vertical clinostats)= R10-12. Evaluation of antibiotic resistance genes via the Comprehensive Antibiotic Resistance Database (CARD) revealed twenty antibiotic resistance genes (ARGs) in each sample within the microbial community ( Fig. 13 B). The antibiotic resistance ontology (ARO) analysis revealed the frequency of 10 specific antibiotic resistance genes (SARGs) in each sample based on the Bray–Curtis distance ( Fig. 13 C). The top four prevalent antibiotic resistance genes included adeF , followed by vanY gene (in vanB cluster), vanT gene (in vanG cluster), and qacG , whereas vanY gene (in vanF cluster) exhibited the lowest abundance in all eight sample groups except HC and VC, which were less abundant in vanW gene (in vanI cluster). However, qacG abundance was slightly higher in the root microbial community than in its soilless-grown media counterparts. Within the soilless medium samples, HC and VC harbored more adeF than their rotating counterparts, while HCR and VCR were more enriched in qacG than their stationary counterparts. The relative abundance of qacG was higher in HCRroots than in HCroots. Advanced sequencing and bioinformatic approaches were employed to identify and quantify the plasmids of each group within the total community DNA of these samples. The relative abundances of bacterial plasmids carrying antibiotic genes were then investigated ( Fig. 13 D). The plasmid ‘ NZ_CP069303.1 ′ was abundant in all sample groups, particularly in the soilless medium samples, with the highest abundance in VCR and HCR. However, ‘ NZ_CP050525.1 ′ and ‘ NZ_CP050529.1 ′, which were almost absent in the soilless medium samples, were prevalent in the root samples. On the contrary, ‘ NZ_LN868939.1 ′ and ‘ NZ_AP014705.1 ’ were significantly more abundant in soilless media, particularly in HC and VC, compared to the microbial community of the kale roots. The proportional representation of specific Insertion Sequence (IS) elements within a total microbial community, as determined by data analysis using the ISfinder database, revealed that the microbial community of the different soilless medium samples harbored the largest abundance of all the IS elements, except ISAzo12, compared to their root counterparts ( Fig. 13 E). HCR and VCR exhibited the highest abundance in the elements ISBrsp1, ISBmu12, and ISMch5, while HC and VC recorded the maximum abundance in ISSphsp7, ISRle4, ISNwi5, ISBdi14, and ISRtr2. Within the root samples, the highest ISAzo12 abundance was observed in the stationary clinostats, particularly in HC. 4. Discussion Developing life support systems for long-distance spaceflight and planet colonization requires understanding plant-microbe symbiosis in altered or microgravity environments. Together, the depth, coverage, and rarefaction-based gene diversity analyses provided strong evidence that the sequencing strategy was sufficient to generate a comprehensive, near‑complete, non‑redundant gene catalog. High sequencing depth ensured robust genome coverage, while the rarefaction curves demonstrated that both gene richness and redundancy had reached stable plateaus ( Li et al., 2025b ; Zaheer et al., 2018 ). These results validate the completeness and reliability of the dataset and provide a solid foundation for downstream analyses, including functional annotation, comparative genomics, and ecological interpretation In this study, the strong positive correlations forming distinct clusters within the G and R groups indicate that samples within each group shared highly similar metagenomic profiles, reflecting consistent biological or environmental conditions within each group, while the weaker between‑group correlations suggest distinct microbial community structures or functional gene compositions ( Mallick et al., 2019 ). In addition, a significant difference in microbiome distribution was observed between stationary and rotating clinostat samples, whether from potting mix or from roots, indicating that clinorotation influenced the composition of the microbiome in kale grown systems. Furthermore, this result highlighted the dependent microbial relationship between potting mix and plant roots. Published reports have shown that clinorotation significantly affected microbial dry and fresh mass, as well as the plant-microbe symbiosis, compared to the static controls in reduced or altered gravity ( Dauzart et al., 2016 ). The number of shared genes in the microbial community was higher between VC and VCR than between HC and HCR. These results showed that clinorotation had a greater impact on horizontal than on vertical clinostats, underscoring the effects of simulated microgravity on kale growth systems. The constant, omnilateral gravitational stimulus applied to the horizontal axis would have more directly stimulated the root, leading to changes in cell polarity and growth that were not seen in vertical clinostats, where the stimulus is mostly consistent along one axis. Wu et al. (2025) , investigating plant-microbe systems, reported that simulated microgravity altered microbial community composition and diversity, especially at lower temperatures, enriching a few microorganisms, suggesting microbial adaptation. Roots interact with rhizosphere soil microorganisms. Identifying the nature of microbial communities in plant habitats is crucial to plant growth and development, as these interactions benefit both microbes and plants. The results of our research indicated that clinorotation had a positive impact on the microbiome of grown kale roots, especially the Bacteroidota, Verrucomicrobiota, Planctomycota , and Ascomycota , compared to the stationary clinostats potting mix. The effects were more pronounced when the clinostats were oriented vertically. These four dominant phyla are crucial in agricultural production. Bacteroidota are often abundant copiotrophs in the rhizosphere, competing for organic resources, reducing disease, and benefiting the hosts ( Martin et al., 2025 ; Gao et al., 2024 ). As for Verrucomicrobiota , they play a crucial role in degrading organic matter, including xylan and complex polysaccharides, which are essential for the carbon cycle and nutrient availability. Nutritional cycling by Verrucomicrobiota may help organic matter absorb and release nutrients ( Cai et al., 2024 ). Planctomycetota are commonly found in soil and have roles in nutrient cycling and are usually associated with carbon fixation. Ascomycota (a fungal phylum) plays diverse roles, including decomposing plant litter, living as endophytes, and aiding in nutrient cycling (Zhang et al., 2022a ). However, the growth of Actinomycota, Mucoromycota, and Pseudomonadota was not favored by the simulated microgravity . Likewise , the growth of minor classes such as Gammaproteobacteria, Planctomycetia, Actinomycetes, Alphaproteobacteria, and Eurotiomycetes was not stimulated by the rotating vertical clinostats. These microbial classes are, however, important in breaking down tough plant residues and releasing nutrients for plants to absorb, nitrogen fixation, biological control, phosphate solubilization, and the production of plant growth-promoting hormones ( Actinomycetota and Alphaproteobacteria ), extending plant's root system and improving its access to water and nutrients, particularly phosphorus ( Mucoromycota ) and soil fertility ( Eurotiomycetes ). Gammaproteobacteria are also of great interest in plant growth as they can act as bioindicators of soil health, with their populations shifting in response to pollutants ( Jia et al., 2025 ; Das et al., 2024 ; Javed et al., 2021 ). Similarly, the fact that the phyla Bdellovibrionota, Verrucomicrobiota , and Armatimonadota exhibited noticeably higher abundance in the R samples (specifically R1-R3) compared to the G samples, suggests that these conditions (horizontal clinorotation) specifically favored the growth of these groups. Members of Bdellovibrionota , specifically Bdellovibrionia, are predatory bacteria that attack and lyse various Gram-negative bacteria. This makes them promising candidates for use as "live antibiotics" or biocontrol agents in agriculture to control harmful root pathogens ( Yan et al., 2025 ; Davis et al., 2024 ; Kamada et al., 2023 Li et al., 2021 ). Verrucomicrobiota , which plays a crucial role in agriculture by promoting plant growth and protecting against soil-borne diseases, is involved in soil carbon cycling and the degradation of complex chemicals. The community structure and abundance of this microbiota can serve as indicators of changes in soil fertility and overall soil quality ( Tan et al., 2025 ; Baliyarsingh et al., 2022 ; Nayak and Mishra, 2020 ). Members of the phylum Armatimonadota are involved in nutrient cycling processes, including sulfur and nitrogen cycles, contributing to overall soil fertility, and their presence indicates adaptability to various agricultural environments ( Zhang et al., 2025a ). Some phyla, such as Pseudomonadota, Bacillota , and Bacteroidota , as well as Mycoplasmatota , were mainly found in root samples and, more importantly, in the HCR roots, indicating that these four phyla are part of the specific rhizosphere microbiome that was selectively enriched by the simulated microgravity conditions created by clinorotation. Plant roots naturally select for specific microbial communities from the bulk potting mix to form their rhizosphere microbiome, often enriching fast-growing, copiotrophic bacteria that can utilize nutrient-rich root exudates ( Acharya et al., 2023 ; Navarro-Noya et al., 2022 ). These phyla may be among the root-associated taxa actively recruited by the kale plant for its growth in the horizontal clinostats during rotation. However, the presence of Mycoplasmatota ( phytoplasma ) indicates that clinorotation might also favor the development of pathogens that cause severe diseases and significantly hinder plant growth ( Kube et al., 2008 ). On the other hand, the abundance of biocontrol agents in the roots, such as members of the Pseudomonadota , is important in helping the plant overcome biotic stress challenges as Pseudomonadota strains produce a wide range of secondary metabolites with antibiotic and antifungal properties (phenazines, phloroglucinols, pyoluteorin, and pyrrolnitrin) and lytic enzymes, inhibiting the growth of various plant pathogens in the rhizosphere. Members of this phylum are effective colonizers of the plant root surface and internal tissues, outcompeting pathogens for nutrients and binding sites ( Chuai et al., 2026 ; Hu et al., 2026 ; Luo et al., 2026 ; Xiao et al., 2025 ; Ruiz et al., 2025 ). Their role in alleviating abiotic stress has also been reported in crops like tomato, cucumber, and Mung bean ( Ullah et al., 2025 ; Abdelrahman et al., 2025 ; Kavya et al., 2024 ; Bo et al., 2023 ). Both Bacillota and Bacteroidota are part of biocontrol agents playing crucial roles in plant growth as plant growth-promoting bacteria, primarily by improving nutrient acquisition, enhancing stress tolerance, and protecting plants against pathogens ( Gao et al., 2024 ; Petrović et al., 2024 ). Furthermore, the Acidobacteriota and Bacteroidota families were abundant in the stationary clinostats, suggesting that the specific conditions of this simulation method differ from those of clinorotation in a way more conducive to these bacteria. In various microgravity models, the absence of gravity-induced convection limits nutrient and waste product transport around bacterial cells, leading to localized nutrient deficiencies and waste accumulation ( Nie et al., 2025 ; Fazayeli et al., 2025 ). The Acidobacteriota and Bacteroidota may be less adapted to these specific mass-transfer limitations, whereas the stationary clinostat arrangement could alleviate this issue, allowing them to maintain bigger populations. The data further demonstrated that clinostat orientation, gravity condition, and the distinction between soilless medium and root samples each exerted significant influence on the expression and relative abundance of carbohydrate‑active enzymes in kale. Rotating systems, particularly the HCR and VCR setups, generally correlated with higher levels of certain CAZymes than non-rotating clinostats, suggesting a distinct plant response to the altered gravitational stimuli in this controlled environment. Su et al. (2021) hypothesized that the enhanced growth rate of carbohydrate-active enzymes could be associated with the upregulation of differentially expressed genes involved in energy catabolism and anabolism, transport, intracellular trafficking, secretion, and vesicular transport. Furthermore, Carbohydrate metabolism is linked to distinct metabolites and differently expressed genes, with CAZYmes and specific metabolic intermediates strongly correlated ( Li et al., 2025a ; Coelho et al., 2022 ; Hao et al., 2019 ). Across all six CAZY classes, the relative abundance of carbohydrate-active enzymes was consistently higher in the root samples (HCroot, VCroot, HCRroot, VCRroot) compared to their corresponding soilless media (HC, VC, HCR, VCR). This reveals that the rhizosphere is a more active site for carbohydrate engineering and breakdown than the bulk soilless medium and can be explored in breeding programs. It has been reported that CAZymes prevalent in the rhizosphere soil of the wild plant species impacted protein folding, membrane integrity, and bacterial cell surface functionality, hence enhancing microbial adaptability and plant-microbe symbiosis ( Refai, 2025 ). This investigation also showed that HCR had the most CAZyme biomarkers while HC had the fewest. The increased production of CAZymes indicates an acceleration of the decomposition and transformation of plant-derived carbon in the soilless-grown media. This observation implies that, unlike the stationary clinostats, the stressful conditions of limited nutrient diffusion and continuous reorientation in the horizontal-rotating clinostats forced the microbial communities in the coco-coir to produce more CAZymes as an adaptive mechanism for survival and nutrient acquisition. For instance, Carbohydrate-binding Modules can help microbes scavenge in microgravity by increasing substrate capture efficiency when diffusion is reduced, and particulate carbohydrates are harder to access. CBMs bind insoluble polysaccharides and keep enzymes localized at the substrate surface, which becomes especially advantageous in microgravity, where fluid mixing is suppressed ( Ribeiro et al., 2024 ), even though more needs to be explored with microgravity‑specific CBM studies. Other studies have reported that microgravity limits molecular transport to and from the cell through diffusion ( Sharma and Curtis, 2022 ). Only one virus phylum ( Uroviricota ) was identified as harboring biomarkers in this study. Gu et al. (2025) reported that Uroviricota is the main viral phylum in agricultural soils, especially in water-unsaturated soils. This virus lyses Actinobacteriota and Proteobacteria , affecting agricultural soils' bacterial community structure and diversity, impacting the rhizosphere's beneficial-pathogenic microorganism balance. Uroviricota phages are also reported to be bacterial gene transfer agents that carry auxiliary metabolic genes or antibiotic resistance genes, and their interaction with host bacteria can facilitate the transfer of these genes within the microbial community. Some Uroviricota bacteriophages can inhibit plant disease-causing bacteria ( Ma et al., 2025 ; Zhang et al., 2024a ; Koonin et al., 2021 ). The most pronounced factor influencing the abundance of glycosyl transferases in this study was the comparison between soilless-grown media and kale roots. Gravity conditions (stationary vs. clinorotation) appeared to have a minimal impact on the relative abundance of total Glycosyl transferases in both the soilless-grown media and root samples. The high abundance of Glycosyl transferases in soilless medium samples observed in this investigation confirmed the study by Alshareef (2024) on Moringa oleifera , which found that carbohydrate-active enzymes of glycosyl transferase class are encoded by fungiome genes in the rhizospheric soil of growing plants. Glycosyl transferases are essential for plant cell wall biosynthesis. Furthermore, they regulate hormone homeostasis and detoxify toxic chemicals to maintain cellular balance and enhance plant growth ( Sonbol and Jalal, 2025 ; Zhang et al., 2024c ). Of the glycoside hydrolases (GH) and carbohydrate esterases (CE), the growth conditions, particularly the use of clinorotation, and the comparison between soilless media and kale roots, had a significant impact on their relative abundance. The introduction of clinorotation (HCR, VCR) significantly lowered the abundance of GH and CE compared to their non-rotating counterparts (HC, VC), indicating the effects of altered gravity conditions on these CAZymes despite their critical roles for soil fertility, nutrient cycling, organic matter decomposition, and enzymatic conversion of plant biomass ( Halmi and Simarani, 2024 ; Wang et al., 2024a ). However, the increase in GH abundance in kale roots under clinorotation highlights their importance in plant roots under stress, as they modify cell walls to support growth and development. These two enzymes enable microbial interactions that improve soil structure and carbon sequestration, boosting biodiversity and promoting climate-resilient agriculture ( Neemisha and Sharma, 2022 ; Berlemont and Martiny, 2016 ). In addition, the sole carbohydrate esterase identified, CE8, appeared to be stable under both constant 1 g and simulated microgravity, suggesting that this specific enzyme may not be fundamental to acute gravity perception or signal transduction. They are reportedly involved in the enzymatic conversion of plant biomass, allowing basic cell growth, division, and overall plant development regardless of gravity ( Larsbrink and Lo Leggio, 2023 ; Armendáriz-Ruiz et al., 2018 ; Qian et al., 2018 ). Furthermore, the increased carbohydrate-binding-module abundance in simulated microgravity might be a stress response and metabolic adaptation to the unique physical conditions, allowing kale plant cells to better adhere to surfaces and efficiently scavenge nutrients under reduced mass transfer conditions, as they are essential in coordinating glycan recognition, general substrate adherence, and structure–function contributions to the catalytic site ( Huang et al., 2024 ; Pasari et al., 2017 ). The overall trend showed that clinorotation had a positive impact on Polysaccharide Lyase expression in both kale roots and the soilless-grown media. This may be a response to the lack of gravitational force, which requires rigid structural support. Polysaccharide lyases are essential for plant growth and development by modifying the pectin component of the plant cell wall ( Chakraborty et al., 2017 ). The identification of reference genomes for unique antibiotic resistance profiles within the microbial community resulted in two major, distinct clusters between soilless-grown medium and kale root microbiomes. This confirms the significant difference in bacterial communities between these two major groups. Several studies reported that microbiome antibiotic resistance profiles and abundance are driven by their bacterial population composition ( Riva et al., 2024 ; Chen et al., 2023 ; Schages et al., 2021 ). The prevalence of the specific reference genome ' CP022915.1 ′ in the soilless-grown media, which corresponded to a strain of Rhodococcus pyridinivorans belonging to the phylum Actinomycetota (GeneBank search in NCBI), is specific to plant-grown media. It serves as an effective agent for environmental bioremediation and acts as a potential vector for the dissemination of antibiotic resistance genes (ARGs) to other bacteria ( Wang et al., 2025 ; Afordoanyi et al., 2023 ; Ivshina et al., 2022 ). The reference genome ' AP025035.1 ′, specific to the root microbiome, is a strain of Citrobacter freundii associated with the phylum Pseudomonadota (GeneBank search in NCBI). C. freundii may store and spread genes that make bacteria resistant to several key antibiotics, such as β-lactams, fluoroquinolones, aminoglycosides, and tetracyclines ( Wang et al., 2024b ; Zhang et al., 2024b ; Zhou et al., 2019 ). Several plasmids were identified and quantified in this study. Plasmids appear to be an ancient and successful method for ensuring soil population survival in spatial and temporal diverse settings with irregular or unique environmental stressors or opportunities ( Garcillán-Barcia et al., 2025 ; Gillett et al., 2025 ; Castañeda-Barba et al., 2024 ). The high abundance of plasmid ' NZ_CP069303.1 ′, belonging to the phylum Pseudomonadota and associated with the presence of antibiotic resistance genes (ARGs) in soilless medium samples, especially those from rotating clinostats, may be due to environmental pressures and conditions that favor its persistence and proliferation. This finding confirms that the Pseudomonadota is one of the dominant bacterial phyla favored in grown kale systems in this study. The antibiotic resistance gene adeF , which was the most prevalent across all the samples, has been reported to possess multidrug-resistant properties, and its host bacteria can tolerate varied pesticide and plastic concentrations ( Ahmed et al., 2025 ). Although the hydroponic system was designed to be a clean, low‑biomass environment, the detection of adeF and vanY may reflect environmental bacteria associated with the coco coir substrate and the kale plant microbiome. Both genes are widely distributed in non‑pathogenic soil and plant‑associated bacteria, and their presence likely reflects natural reservoirs of efflux pump and cell‑wall remodeling genes rather than contamination from the water or the seeds themselves ( Sun et al., 2026 ; Saral Sariyer and Sariyer, 2025 ). Furthermore, all the other ARGs were found in very low relative abundance in the kale root microbiome, except qacG . This key finding suggests that qacG , which encodes the quaternary ammonium compounds, may be particularly well-adapted to the kale root environment, in contrast to the other ARGs. This gene has been identified as potentially tolerant to disinfectants, as well as to the intercalating dye ethidium bromide, diamidines, and biguanides ( Hamar et al., 2025 ; Wassenaar et al., 2015 ). Additionally, reports indicated that ARGs are carried by mobile genetic elements within vast bacterial populations ( Li et al., 2024 ; Saak et al., 2020 ). This investigation revealed that the soilless medium samples harbored the largest abundance of the specific Insertion Sequence (IS), corroborating the predominance of the identified plasmids in the soilless samples as IS catalyze plasmid adaptability, stability, and horizontal gene transfer in stressful environments, affecting microbial community plasmid abundance ( Wedel et al., 2023 ; Zhang et al., 2022b ; Brito, 2021 ; Ali et al., 2020 ; Vandecraen et al., 2017 ). The differences in soilless dominance between the normal, the rotating 1 g control, and simulated gravity conditions suggest that the rotating and stationary conditions created distinct environments for the proliferation of their associated bacterial species. The antibiotic resistance genes and mobile genetic elements in the grown kale systems microbiome provided strong indirect evidence of Horizontal Gene Transfer (HGT) occurring within the microbial communities represented by samples G1–G12 and R1–R12. Although metagenomic data cannot directly observe gene transfer events, the co‑occurrence of plasmids, insertion sequences, and antibiotic resistance genes, combined with the clustering of samples based on these mobile elements, provides compelling evidence that HGT is actively contributing to the distribution of functional genes across the community. This pattern is consistent with environments where microbial populations experience selective pressures (e.g., nutrient stress), which often accelerate HGT‑mediated adaptation ( Zhang et al., 2025b ; Olsen and Riber, 2025 ; Tokuda and Shintani, 2024 ; Li et al., 2022 ). 5. Conclusion This was the first study to evaluate and compare microbial community profiles in soilless medium-grown Starbor kale systems under gravity, simulated microgravity, and a rotating 1 g control using metagenomics. The results revealed clear differences in the composition, diversity, and functional structure of the microbial communities associated with the kale growth systems. While simulated microgravity and the rotating 1 g control favored the enrichment of specific microbial groups, a broader and more heterogeneous distribution was observed under normal gravity. Moreover, the soilless‑grown medium microbiome was less affected by clinorotation than the roots, which exhibited more pronounced shifts in community structure. The phyla Pseudomonadota and Actinomycetota were highly prevalent across all samples, particularly in the coco coir under simulated gravity. The CAZymes glycoside hydrolases and carbohydrate esterases were highly abundant in the coco coir samples under normal gravity, whereas carbohydrate‑binding modules were comparatively enriched under simulated gravity. The four most prevalent antibiotic resistance genes detected across the samples were adeF, vanY, vanT , and qacG . The shared and unique microbial taxa, biomarkers, carbohydrate-active enzymes, antibiotic resistance genes, and specific insertion sequence elements identified and characterized in the current study served as invaluable tools for gaining insight into kale cultivation and are of paramount importance in leafy green agricultural systems. These results provide a foundation for future breeding programs of Starbor kale for vegetable production systems, especially on the International Space Station. Funding This research was funded in its entirety by NASA (National Aeronautics and Space Administration), USA, under grant number 80NSSC22K0869, and by the Department of Education Title III Part B HBCU (Course-Based Undergraduate Research Experiences Program at Bowie State University), grant number GR23000897. Ethics approval and consent to participate Not applicable CRediT authorship contribution statement Labode Hospice Stevenson Naitchede: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Onyinye C. Ihearahu: Writing – review & editing, Visualization, Methodology, Data curation. Kishan Saha: Writing – review & editing, Visualization, Methodology, Data curation, Investigation. David O. Igwe: Writing – review & editing, Visualization, Data curation, Investigation. Jie Yan: Writing – review & editing, Investigation, Conceptualization, Funding acquisition. Anne A. Osano: Writing – review & editing, Investigation, Supervision, Methodology, Conceptualization, Funding acquisition. Supriyo Ray: Writing – review & editing, Investigation, Supervision, Methodology, Conceptualization, Funding acquisition. George Ude: Writing – review & editing, Investigation, Supervision, Project administration, Funding acquisition, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements Bowie State University is hereby thanked for providing the growth chamber, greenhouses, and laboratories for this investigation via the Natural Science Department and the Title III grant received by George Ude. Special thanks to Professor Aggrey Bernard Nyende for reading and editing this manuscript. Footnotes Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.crmicr.2026.100592 . Contributor Information Supriyo Ray, Email: [email protected]. George Ude, Email: [email protected]. Appendix. Supplementary materials mmc1.docx (926.3KB, docx) mmc2.xls (23KB, xls) mmc3.xls (22KB, xls) mmc4.xls (27.5KB, xls) mmc5.xls (444.5KB, xls) mmc6.xls (21.5KB, xls) mmc7.xls (328.5KB, xls) mmc8.xls (466.5KB, xls) mmc9.xls (1.9MB, xls) mmc10.xls (27.5KB, xls) Supplementary materials mmc11.xls (26KB, xls) Data availability Data will be made available on request. References Abdelrahman M., Jogaiah S., Abdelmoteleb M., Fokar M., Nguyen H.T., Tran L.-S.P. Deciphering crop-specific rhizobacteriome assembly in cotton, sorghum, and soybean under hot semi-arid field conditions in Texas. Environ. Microb. 2025;20:105. doi: 10.1186/s40793-025-00763-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Acharya S.M., Yee M.O., Diamond S., Andeer P.F., Baig N.F., Aladesanmi O.T., Northen T.R., Banfield J.F., Chakraborty R. Fine scale sampling reveals early differentiation of rhizosphere microbiome from bulk soil in young Brachypodium plant roots. ISME Commun. 2023;3:54. doi: 10.1038/s43705-023-00265-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Afordoanyi D.M., Akosah Y.A., Shnakhova L., Saparmyradov K., Diabankana R.G.C., Validov S. Biotechnological key genes of the rhodococcus erythropolis MGMM8 genome: genes for bioremediation, antibiotics, plant protection, and growth stimulation. Microorganisms. 2023;12:88. doi: 10.3390/microorganisms12010088. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Afridi M.S., Javed M.A., Ali S., De Medeiros F.H.V., Ali B., Salam A., Sumaira, Marc R.A., Alkhalifah D.H.M., Selim S., Santoyo G. New opportunities in plant microbiome engineering for increasing agricultural sustainability under stressful conditions. Front. Plant Sci. 2022;13 doi: 10.3389/fpls.2022.899464. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ahmed A., Mahmud S., Tania Khatun Mst., Firoz Ali Md., Rony Akter M., Mohiuddin A.K.M. Biodegradation of plastics and pesticides by soil bacteria in Bangladesh: insights into antibiotic resistance and potential therapeutic targets. J. Gen. Eng. Biotechnol. 2025;23 doi: 10.1016/j.jgeb.2025.100532. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ali N., Lin Y., Qing Z., Xiao D., Ud Din A., Ali I., Lian T., Chen B., Wen R. The role of agriculture in the dissemination of class 1 integrons, antimicrobial resistance, and diversity of their gene cassettes in southern China. Genes (Basel) 2020;11:1014. doi: 10.3390/genes11091014. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Alori E.T., Dare M.O., Babalola O.O. In: Sustainable Agriculture Reviews, Sustainable Agriculture Reviews. Lichtfouse E., editor. Springer International Publishing; Cham: 2017. Microbial inoculants for soil quality and plant health; pp. 281–307. [ DOI ] [ Google Scholar ] Alshareef S.A. Metabolic analysis of the CAZy class glycosyltransferases in rhizospheric soil fungiome of the plant species moringa oleifera. Saudi J. Biol. Sci. 2024;31 doi: 10.1016/j.sjbs.2024.103956. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Al-Shehbaz I.A. The Brassicaceae then and now: advancements in the past three decades, a review. Ann. Bot. 2025:mcaf055. doi: 10.1093/aob/mcaf055. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Armendáriz-Ruiz M., Rodríguez-González J.A., Camacho-Ruíz R.M., Mateos-Díaz J.C. In: Lipases and Phospholipases, Methods in Molecular Biology. Sandoval G., editor. Springer New York; New York, NY: 2018. Carbohydrate esterases: an overview; pp. 39–68. [ DOI ] [ PubMed ] [ Google Scholar ] Bäckhed F., Roswall J., Peng Y., Feng Q., Jia H., Kovatcheva-Datchary P., Li Y., Xia Y., Xie H., Zhong H., Khan M.T., Zhang J., Li J., Xiao L., Al-Aama J., Zhang D., Lee Y.S., Kotowska D., Colding C., Tremaroli V., Yin Y., Bergman S., Xu X., Madsen L., Kristiansen K., Dahlgren J., Wang J. Dynamics and stabilization of the Human gut microbiome during the first year of life. Cell Host Microbe. 2015;17:852. doi: 10.1016/j.chom.2015.05.012. [ DOI ] [ PubMed ] [ Google Scholar ] Baliyarsingh B., Dash B., Nayak S., Nayak S.K. In: Advances in Agricultural and Industrial Microbiology. Nayak S.K., Baliyarsingh B., Mannazzu I., Singh A., Mishra B.B., editors. Springer Nature Singapore; Singapore: 2022. Soil Verrucomicrobia and their role in sustainable agriculture; pp. 105–124. [ DOI ] [ Google Scholar ] Basu A., Prasad P., Das S.N., Kalam S., Sayyed R.Z., Reddy M.S., El Enshasy H. Plant growth promoting rhizobacteria (PGPR) as green bioinoculants: recent developments, constraints, and prospects. Sustainability. 2021;13:1140. doi: 10.3390/su13031140. [ DOI ] [ Google Scholar ] Berlemont R., Martiny A.C. Glycoside hydrolases across environmental microbial communities. PLoS Comput. Biol. 2016;12 doi: 10.1371/journal.pcbi.1005300. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Bo H., Li Z., Jin D., Xu M., Zhang Q. Fertilizer management methods affect bacterial community structure and diversity in the maize rhizosphere soil of a coal mine reclamation area. Ann. Microbiol. 2023;73:24. doi: 10.1186/s13213-023-01729-4. [ DOI ] [ Google Scholar ] Brito I.L. Examining horizontal gene transfer in microbial communities. Nat. Rev. Microbiol. 2021;19:442–453. doi: 10.1038/s41579-021-00534-7. [ DOI ] [ PubMed ] [ Google Scholar ] Buchfink B., Xie C., Huson D.H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods. 2015;12:59–60. doi: 10.1038/nmeth.3176. [ DOI ] [ PubMed ] [ Google Scholar ] Cai S., Wang W., Sun L., Li Yumei, Sun Z., Gao Z., Zhang J., Li Yan, Wei D. Bacteria affect the distribution of soil-dissolved organic matter on the slope: a long-term experiment in black soil erosion. Agriculture. 2024;14:352. doi: 10.3390/agriculture14030352. [ DOI ] [ Google Scholar ] Castañeda-Barba S., Top E.M., Stalder T. Plasmids, a molecular cornerstone of antimicrobial resistance in the one health era. Nat. Rev. Microbiol. 2024;22:18–32. doi: 10.1038/s41579-023-00926-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Chakraborty S., Rani A., Dhillon A., Goyal A. Current Developments in Biotechnology and Bioengineering. Elsevier; 2017. Polysaccharide lyases; pp. 527–539. [ DOI ] [ Google Scholar ] Chen P., Yu K., He Y. The dynamics and transmission of antibiotic resistance associated with plant microbiomes. Environ. Int. 2023;176 doi: 10.1016/j.envint.2023.107986. [ DOI ] [ PubMed ] [ Google Scholar ] Chuai H., Li G., Tao L., Ouyang L., Ruan R., Wei Z., Whalen J., Nielsen U.N., Liu T., Li H. Pseudomonadota bridge cross-trophic interactions to suppress plant pathogens. ISME J. 2026;20 doi: 10.1093/ismejo/wrag011. wrag011. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Coelho D., Ribeiro D., Osório H., De Almeida A.M., Prates J.A.M. Integrated Omics analysis of pig muscle metabolism under the effects of dietary chlorella vulgaris and exogenous enzymes. Sci. Rep. 2022;12 doi: 10.1038/s41598-022-21466-z. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Cotillard A., Kennedy S.P., Kong L.C., Prifti E., Pons N., Le Chatelier E., Almeida M., Quinquis B., Levenez F., Galleron N., Gougis S., Rizkalla S., Batto J.-M., Renault P., ANR MicroObes consortium. Doré J., Zucker J.-D., Clément K., Ehrlich S.D., ANR MicroObes consortium members. Blottière H., Leclerc M., Juste C., De Wouters T., Lepage P., Fouqueray C., Basdevant A., Henegar C., Godard C., Fondacci M., Rohia A., Hajduch F., Weissenbach J., Pelletier E., Le Paslier D., Gauchi J.-P., Gibrat J.-F., Loux V., Carré W., Maguin E., Van De Guchte M., Jamet A., Boumezbeur F., Layec S. Dietary intervention impact on gut microbial gene richness. Nature. 2013;500:585–588. doi: 10.1038/nature12480. [ DOI ] [ PubMed ] [ Google Scholar ] Cui J., Yi Z., Chen D., Fu Y., Liu H. Microgravity stress alters bacterial community assembly and co-occurrence networks during wheat seed germination. Sci. Total Environ. 2023;890 doi: 10.1016/j.scitotenv.2023.164147. [ DOI ] [ PubMed ] [ Google Scholar ] Das R., Bharadwaj P., Thakur D. Insights into the functional role of Actinomycetia in promoting plant growth and biocontrol in tea (Camellia sinensis) plants. Arch. Microbiol. 2024;206:65. doi: 10.1007/s00203-023-03789-1. [ DOI ] [ PubMed ] [ Google Scholar ] Dauzart A.J.C., Vandenbrink J.P., Kiss J.Z. The effects of clinorotation on the host plant, Medicago truncatula, and its microbial symbionts. Front. Astron. Space Sci. 2016;3 doi: 10.3389/fspas.2016.00003. [ DOI ] [ Google Scholar ] Davis S.C., Cerra J., Williams L.E. Comparative genomics of obligate predatory bacteria belonging to phylum bdellovibrionota highlights distribution and predicted functions of lineage-specific protein families. mSphere. 2024;9 doi: 10.1128/msphere.00680-24. -24. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] De Micco V., Aronne G., Caplin N., Carnero-Diaz E., Herranz R., Horemans N., Legué V., Medina F.J., Pereda-Loth V., Schiefloe M., De Francesco S., Izzo L.G., Le Disquet I., Kittang Jost A.-I. Perspectives for plant biology in space and analogue environments. npj Microgravity. 2023;9:67. doi: 10.1038/s41526-023-00315-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] De Pascale S., Arena C., Aronne G., De Micco V., Pannico A., Paradiso R., Rouphael Y. Biology and crop production in Space environments: challenges and opportunities. Life Sci. Space Res. 2021;29:30–37. doi: 10.1016/j.lssr.2021.02.005. [ DOI ] [ PubMed ] [ Google Scholar ] Delgado-Baquerizo M., Reich P.B., Trivedi C., Eldridge D.J., Abades S., Alfaro F.D., Bastida F., Berhe A.A., Cutler N.A., Gallardo A., García-Velázquez L., Hart S.C., Hayes P.E., He J.-Z., Hseu Z.-Y., Hu H.-W., Kirchmair M., Neuhauser S., Pérez C.A., Reed S.C., Santos F., Sullivan B.W., Trivedi P., Wang J.-T., Weber-Grullon L., Williams M.A., Singh B.K. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 2020;4:210–220. doi: 10.1038/s41559-019-1084-y. [ DOI ] [ PubMed ] [ Google Scholar ] Desai S.A., Patel V.P., Bhosle K., Nagare S., Thombare K., Hashem A., Abd-Allah E.F., Parray J.A. In: Progress in Soil Microbiome Research, Progress in Soil Science. Parray J.A., editor. Springer Nature; Switzerland, Cham: 2024. Microbiome and ecosystem approaches; pp. 37–52. [ DOI ] [ Google Scholar ] Ehrlich J.W., Massa G., Wheeler R., Gill T.R., Quincy C., Roberson L., Binsted K., Morrow R. AIAA SPACE and Astronautics Forum and Exposition. Presented at the AIAA SPACE and Astronautics Forum and Exposition. American Institute of Aeronautics and Astronautics; Orlando, FL: 2017. Plant growth optimization by vegetable production system in HI-SEAS analog habitat. [ DOI ] [ Google Scholar ] Fahad S., Saud S., Nawaz T., Gu L., Ahmad M., Zhou R., editors. Environment, Climate, Plant and Vegetation Growth. 1st ed. 2024. Springer Nature; Switzerland, Cham: 2024. [ DOI ] [ Google Scholar ] Fazayeli H., Daigh A.L.M., Palmer C., Pitla S., Jones D., Ge Y. Space agriculture: a comprehensive systems-level review of challenges and opportunities. Agriculture. 2025;15:2541. doi: 10.3390/agriculture15242541. [ DOI ] [ Google Scholar ] Feng Q., Liang S., Jia H., Stadlmayr A., Tang L., Lan Z., Zhang D., Xia H., Xu Xiaoying, Jie Z., Su L., Li Xiaoping, Li Xin, Li J., Xiao L., Huber-Schönauer U., Niederseer D., Xu Xun, Al-Aama J.Y., Yang H., Wang Jian, Kristiansen K., Arumugam M., Tilg H., Datz C., Wang Jun. Gut microbiome development along the colorectal adenoma–carcinoma sequence. Nat. Commun. 2015;6:6528. doi: 10.1038/ncomms7528. [ DOI ] [ PubMed ] [ Google Scholar ] Fu L., Niu B., Zhu Z., Wu S., Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28:3150–3152. doi: 10.1093/bioinformatics/bts565. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Fu Y., Liu Hui, Shao L., Wang M., Berkovich Y.A., Erokhin A.N., Liu Hong. A high-performance ground-based prototype of horn-type sequential vegetable production facility for life support system in space. Adv. Space Res. 2013;52:97–104. doi: 10.1016/j.asr.2013.03.020. [ DOI ] [ Google Scholar ] Galanty A., Kłos P., Prochownik E., Paśko P., Skalski T., Podsiadły R., Zagrodzki P. Cytotoxic and antioxidant properties and profile of active compounds in kale and lupine sprouts supplemented with γ-polyglutamic acid during sprouting. Appl. Sci. 2025;15:2813. doi: 10.3390/app15052813. [ DOI ] [ Google Scholar ] Gallardo-Navarro O., Aguilar-Salinas B., Rocha J., Olmedo-Álvarez G. Higher-order interactions and emergent properties of microbial communities: the power of synthetic ecology. Heliyon. 2024;10 doi: 10.1016/j.heliyon.2024.e33896. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gao L., Ma J.-B., Huang Y., Muhammad M., Lian H.-T., Shurigin V., Egamberdieva D., Li W.-J., Li L. Insight into endophytic microbial diversity in two halophytes and plant beneficial attributes of Bacillus swezeyi. Front. Microbiol. 2024;15 doi: 10.3389/fmicb.2024.1447755. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Garcillán-Barcia M.P., de la Cruz F., Rocha E.P.C. The extended mobility of plasmids. Nucleic Acids Res. 2025;53:gkaf652. doi: 10.1093/nar/gkaf652. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] German D.A., Hendriks K.P., Koch M.A., Lens F., Lysak M.A., Bailey C.D., Mummenhoff K., Al-Shehbaz I.A. An updated classification of the brassicaceae (Cruciferae) PK. 2023;220:127–144. doi: 10.3897/phytokeys.220.97724. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Getahun S., Kefale H., Gelaye Y. Application of precision agriculture technologies for sustainable crop production and environmental sustainability: a systematic review. Sci. World J. 2024;2024 doi: 10.1155/2024/2126734. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gillett D.L., Selinidis M., Seamons T., George D., Igwe A.N., Del Valle I., Egbert R.G., Hofmockel K.S., Johnson A.L., Matthews K.R.W., Masiello C.A., Stadler L.B., Chappell J., Silberg J.J. A roadmap to understanding and anticipating microbial gene transfer in soil communities. Microbiol. Mol. Biol. Rev. 2025;89 doi: 10.1128/mmbr.00225-24. -24. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Glick B.R., Gamalero E. Recent developments in the study of plant microbiomes. Microorganisms. 2021;9:1533. doi: 10.3390/microorganisms9071533. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Gu H., Hu X., Zhang J., Li Y., Yu Z., Liu J., Sui Y., Jin J., Liu X., Wang G. Biogeographic patterns of viral communities, ARG profiles and virus-ARG associations in adjacent paddy and upland soils across black soil region. J. Hazard. Mater. 2025;485 doi: 10.1016/j.jhazmat.2024.136909. [ DOI ] [ PubMed ] [ Google Scholar ] Halmi M.F.A., Simarani K. Response of soil microbial glycoside hydrolase family 6 cellulolytic population to lignocellulosic biochar reveals biochar stability toward microbial degradation. J. Environ. Qual. 2024;53:546–551. doi: 10.1002/jeq2.20588. [ DOI ] [ PubMed ] [ Google Scholar ] Hamar F., Loncaric I., Bernreiter-Hofer T., Cabal Rosel A., Stöger A., Palle-Reisch M., Ruppitsch W., Kaesbohrer A., Buzanich-Ladinig A., Bluemlinger M., Schwarz L. MRSA in pig farming: the emerging role of flies in antimicrobial resistance: a cross-sectional study. Porc. Health Manag. 2025;11:46. doi: 10.1186/s40813-025-00459-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hao H., Zhang J., Wang H., Wang Q., Chen M., Juan J., Feng Z., Chen H. Comparative transcriptome analysis reveals potential fruiting body formation mechanisms in Morchella importuna. AMB Expr. 2019;9:103. doi: 10.1186/s13568-019-0831-4. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hasan A., Tabassum B., Hashim M., Khan N. Role of plant growth promoting rhizobacteria (PGPR) as a plant growth enhancer for sustainable agriculture: a review. Bacteria. 2024;3:59–75. doi: 10.3390/bacteria3020005. [ DOI ] [ Google Scholar ] Hasenstein K.H., Miklave N.M. Hydroponics for plant cultivation in space – a white paper. Life Sci. Space Res. 2024;43:13–21. doi: 10.1016/j.lssr.2024.06.004. [ DOI ] [ PubMed ] [ Google Scholar ] Hloušková P., Mandáková T., Pouch M., Trávníček P., Lysak M.A. The large genome size variation in the Hesperis clade was shaped by the prevalent proliferation of DNA repeats and rarer genome downsizing. Ann. Bot. 2019;124:103–120. doi: 10.1093/aob/mcz036. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hosamani R., Swamy B.K., Sathasivam M., Dsouza A., Ashiq I.M. Cocopeat supplementation negates lunar soil simulant-induced baneful phenotypic and biochemical changes in crop seedlings. Acta Astronaut. 2024;220:416–426. doi: 10.1016/j.actaastro.2024.05.001. [ DOI ] [ Google Scholar ] Hu S., Dai Y., Chen H., Cui Y., Zhai Y., Zhang Y., Wang B., Li X., Chen J. Diversity, composition, and Co-occurrence network of rhizosphere and endophytic bacterial communities in two growth stages of Leonurus japonicus. Curr. Microbiol. 2026;83:113. doi: 10.1007/s00284-025-04700-5. [ DOI ] [ PubMed ] [ Google Scholar ] Huang Y., Liu L., Wang R., Jiang T., Yu Q., Wang E., Yuan H. Functional identification of carbohydrate-binding module 13 and its application to quantification of hemicellulose in gramineous plants. Int. J. Biol. Macromol. 2024;282 doi: 10.1016/j.ijbiomac.2024.136752. [ DOI ] [ PubMed ] [ Google Scholar ] Huerta-Cepas J., Szklarczyk D., Forslund K., Cook H., Heller D., Walter M.C., Rattei T., Mende D.R., Sunagawa S., Kuhn M., Jensen L.J., von Mering C., Bork P. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–D293. doi: 10.1093/nar/gkv1248. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Hummerick M.E., Khodadad C.L.M., Dixit A.R., Spencer L.E., Maldonado-Vasquez G.J., Gooden J.L., Spern C.J., Fischer J.A., Dufour N., Wheeler R.M., Romeyn M.W., Smith T.M., Massa G.D., Zhang Y. Spatial characterization of microbial communities on multi-species leafy greens grown simultaneously in the vegetable production systems on the International Space Station. Life. 2021;11:1060. doi: 10.3390/life11101060. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Huson D.H., Mitra S., Ruscheweyh H.-J., Weber N., Schuster S.C. Integrative analysis of environmental sequences using MEGAN4. Genome Res. 2011;21:1552–1560. doi: 10.1101/gr.120618.111. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Huws S.A., Creevey C.J., Oyama L.B., Mizrahi I., Denman S.E., Popova M., Muñoz-Tamayo R., Forano E., Waters S.M., Hess M., Tapio I., Smidt H., Krizsan S.J., Yáñez-Ruiz D.R., Belanche A., Guan L., Gruninger R.J., McAllister T.A., Newbold C.J., Roehe R., Dewhurst R.J., Snelling T.J., Watson M., Suen G., Hart E.H., Kingston-Smith A.H., Scollan N.D., Do Prado R.M., Pilau E.J., Mantovani H.C., Attwood G.T., Edwards J.E., McEwan N.R., Morrisson S., Mayorga O.L., Elliott C., Morgavi D.P. Addressing global ruminant agricultural challenges through understanding the rumen microbiome: past, present, and future. Front. Microbiol. 2018;9:2161. doi: 10.3389/fmicb.2018.02161. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ivshina I., Bazhutin G., Tyumina E. Rhodococcus strains as a good biotool for neutralizing pharmaceutical pollutants and obtaining therapeutically valuable products: through the past into the future. Front. Microbiol. 2022;13 doi: 10.3389/fmicb.2022.967127. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Javed Z., Tripathi G.D., Mishra M., Dashora K. Actinomycetes – the microbial machinery for the organic-cycling, plant growth, and sustainable soil health. Biocatal. Agric. Biotechnol. 2021;31 doi: 10.1016/j.bcab.2020.101893. [ DOI ] [ Google Scholar ] Jehani M.D., Singh S., A T.S., Kumar D., Kumar G. Rhizobiome. Elsevier; 2023. Azospirillum—a free-living nitrogen-fixing bacterium; pp. 285–308. [ DOI ] [ Google Scholar ] Jia B., Raphenya A.R., Alcock B., Waglechner N., Guo P., Tsang K.K., Lago B.A., Dave B.M., Pereira S., Sharma A.N., Doshi S., Courtot M., Lo R., Williams L.E., Frye J.G., Elsayegh T., Sardar D., Westman E.L., Pawlowski A.C., Johnson T.A., Brinkman F.S.L., Wright G.D., McArthur A.G. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 2017;45:D566–D573. doi: 10.1093/nar/gkw1004. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Jia Z., Li C., Zhang S., Tang Y., Ma S., Liu X., Zhang J. Microbial inoculants modify the functions of soil microbes to optimize plant growth at abandoned mine sites. J. Environ. Sci. 2025;154:678–690. doi: 10.1016/j.jes.2024.10.002. [ DOI ] [ PubMed ] [ Google Scholar ] Kamada S., Wakabayashi R., Naganuma T. Phylogenetic revisit to a review on predatory bacteria. Microorganisms. 2023;11:1673. doi: 10.3390/microorganisms11071673. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kanehisa M., Furumichi M., Tanabe M., Sato Y., Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 2017;45:D353–D361. doi: 10.1093/nar/gkw1092. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Karlsson F.H., Fåk F., Nookaew I., Tremaroli V., Fagerberg B., Petranovic D., Bäckhed F., Nielsen J. Symptomatic atherosclerosis is associated with an altered gut metagenome. Nat. Commun. 2012;3:1245. doi: 10.1038/ncomms2266. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Karlsson F.H., Tremaroli V., Nookaew I., Bergström G., Behre C.J., Fagerberg B., Nielsen J., Bäckhed F. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature. 2013;498:99–103. doi: 10.1038/nature12198. [ DOI ] [ PubMed ] [ Google Scholar ] Kavya T., Govindasamy V., Suman A., Abraham G. In: Plant Holobiome Engineering For Climate-Smart Agriculture, Sustainable Plant Nutrition in a Changing World. Sayyed R.Z., Ilyas N., editors. Springer Nature; Singapore, Singapore: 2024. Plant–Actinobacteria interactions for biotic and abiotic stress management in crops; pp. 441–463. [ DOI ] [ Google Scholar ] Khalid W., Iqra, Afzal F., Rahim M.A., Abdul Rehman A., Faiz Ul Rasul H., Arshad M.S., Ambreen S., Zubair M., Safdar S., Al-Farga A., Refai M. Industrial applications of kale (Brassica oleracea var. sabellica) as a functional ingredient: a review. Int. J. Food Prop. 2023;26:489–501. doi: 10.1080/10942912.2023.2168011. [ DOI ] [ Google Scholar ] Kiss J.Z., Wolverton C., Wyatt S.E., Hasenstein K.H., Van Loon J.J.W.A. Comparison of microgravity analogs to spaceflight in studies of plant growth and development. Front. Plant Sci. 2019;10:1577. doi: 10.3389/fpls.2019.01577. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Koonin E.V., Dolja V.V., Krupovic M., Kuhn J.H. Viruses defined by the position of the virosphere within the replicator space. Microbiol. Mol. Biol. Rev. 2021;85 doi: 10.1128/MMBR.00193-20. -20. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kube M., Schneider B., Kuhl H., Dandekar T., Heitmann K., Migdoll A.M., Reinhardt R., Seemüller E. The linear chromosome of the plant-pathogenic mycoplasma “Candidatus phytoplasma mali. BMC Genom. 2008;9:306. doi: 10.1186/1471-2164-9-306. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kultima J.R., Sunagawa S., Li J., Chen W., Chen H., Mende D.R., Arumugam M., Pan Q., Liu B., Qin J., Wang J., Bork P. MOCAT: a metagenomics assembly and gene prediction toolkit. PLoS ONE. 2012;7 doi: 10.1371/journal.pone.0047656. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Kumar V., Singh K., Shah M.P., Singh A.K., Kumar A., Kumar Y. Wastewater Treatment. Elsevier; 2021. Application of Omics technologies for microbial community structure and function analysis in contaminated environment; pp. 1–40. [ DOI ] [ Google Scholar ] Kumar V., Singh S., Jakhwal R., Singh B., Tomar H. Space farming : need for fresh vegetable crop. Ann. Horticult. 2023;16:65–71. doi: 10.5958/0976-4623.2023.00012.9. [ DOI ] [ Google Scholar ] Larsbrink J., Lo Leggio L. Glucuronoyl esterases – enzymes to decouple lignin and carbohydrates and enable better utilization of renewable plant biomass. Essays Biochem. 2023;67:493–503. doi: 10.1042/EBC20220155. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] 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. 2015;31:1674–1676. doi: 10.1093/bioinformatics/btv033. [ DOI ] [ PubMed ] [ Google Scholar ] Li J., Luo Y., Yu B., He J., Wang H., Wang Q., Chen D. Multi-omics analysis provides insights into mechanisms of intestinal fungi adaptation to dietary carbohydrates. Curr. Res. Microb. Sci. 2025;9 doi: 10.1016/j.crmicr.2025.100451. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li J., Jia H., Cai X., Zhong H., Feng Q., Sunagawa S., Arumugam M., Kultima J.R., Prifti E., Nielsen T., Juncker A.S., Manichanh C., Chen B., Zhang W., Levenez F., Wang Juan, Xu X., Xiao L., Liang S., Zhang D., Zhang Z., Chen W., Zhao H., Al-Aama J.Y., Edris S., Yang H., Wang Jian, Hansen T., Nielsen H.B., Brunak S., Kristiansen K., Guarner F., Pedersen O., Doré J., Ehrlich S.D., Bork P., Wang Jun. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 2014;32:834–841. doi: 10.1038/nbt.2942. [ DOI ] [ PubMed ] [ Google Scholar ] Li Q.-M., Zhou Y.-L., Wei Z.-F., Wang Y. Phylogenomic insights into distribution and adaptation of bdellovibrionota in marine waters. Microorganisms. 2021;9:757. doi: 10.3390/microorganisms9040757. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Leonard J.M., Toro D.D. Defining the microbiome components (Bacteria, Viruses, Fungi) and microbiome geodiversity. Surg. Infect. (Larchmt) 2023;24:208–212. doi: 10.1089/sur.2023.014. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li X., Tian C., Zhuang D., Shi X., Tian L., Bai L., Gao H., Zhou H., Zhao F., Dai M., Zhu L., Yu J., Wu Q., Liu X., Zhang T., Sang J., Li T., Luo Y., Tang Z., Sahu S.K., Xu X., Wang J., Liu H., Xiao L., Kristiansen K., Zhang Z. A unified catalog of 14,062 microbial species reference genomes provides new insight into the gut microbiota in high-altitude mammals. Microbiome. 2025;13:236. doi: 10.1186/s40168-025-02232-5. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li X., Yang Z., Zhang G., Si S., Wu X., Cai L. Plasmid genomes reveal the distribution, abundance, and organization of mercury-related genes and their Co-distribution with antibiotic resistant genes in gammaproteobacteria. Genes (Basel) 2022;13:2149. doi: 10.3390/genes13112149. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Li Y., Li R., Hou J., Sun X., Wang Y., Li L., Yang F., Yao Y., An Y. Mobile genetic elements affect the dissemination of antibiotic resistance genes (ARGs) of clinical importance in the environment. Environ. Res. 2024;243 doi: 10.1016/j.envres.2023.117801. [ DOI ] [ PubMed ] [ Google Scholar ] Łukaszyk A., Kwiecień I., Kanik A., Blicharska E., Tatarczak-Michalewska M., Białowąs W., Czarnek K., Szopa A. Nutritional, therapeutic, and functional food perspectives of Kale (Brassica oleracea var. acephala): an integrative review. Molecules. 2025;30:4214. doi: 10.3390/molecules30214214. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Luo Y., Ding H., Mao X., Kang Z., Li B., Zhou Y. Mechanistic insights into rhizosphere microbiome assembly in Pinus tabuliformis: the role of cross–kingdom interactions and soil salinity gradients. Fungal Biol. 2026;130 doi: 10.1016/j.funbio.2025.101695. [ DOI ] [ PubMed ] [ Google Scholar ] Ma Y., Dong X., Sun Y., Li B., Ma H., Li H., Zhao X., Ran S., Zhang J., Ye Y., Li J. Diversity and functional roles of viral communities in gene transfer and antibiotic resistance in aquaculture waters and microplastic biofilms. Environ. Pollut. 2025;381 doi: 10.1016/j.envpol.2025.126636. [ DOI ] [ PubMed ] [ Google Scholar ] Mabry M.E., Turner-Hissong S.D., Gallagher E.Y., McAlvay A.C., An H., Edger P.P., Moore J.D., Pink D.A.C., Teakle G.R., Stevens C.J., Barker G., Labate J., Fuller D.Q., Allaby R.G., Beissinger T., Decker J.E., Gore M.A., Pires J.C. The evolutionary history of wild, domesticated, and feral brassica oleracea (Brassicaceae) Mol. Biol. Evol. 2021;38:4419–4434. doi: 10.1093/molbev/msab183. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Maffei M.E., Balestrini R., Costantino P., Lanfranco L., Morgante M., Battistelli A., Del Bianco M. The physiology of plants in the context of space exploration. Commun. Biol. 2024;7:1311. doi: 10.1038/s42003-024-06989-7. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Mallick H., Franzosa E.A., Mclver L.J., Banerjee S., Sirota-Madi A., Kostic A.D., Clish C.B., Vlamakis H., Xavier R.J., Huttenhower C. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 2019;10:3136. doi: 10.1038/s41467-019-10927-1. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Maron P.-A., Sarr A., Kaisermann A., Lévêque J., Mathieu O., Guigue J., Karimi B., Bernard L., Dequiedt S., Terrat S., Chabbi A., Ranjard L. High microbial diversity promotes soil ecosystem functioning. Appl. Environ. Microbiol. 2018;84 doi: 10.1128/AEM.02738-17. -17. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Martin H., Rogers L.A., Moushtaq L., Brindley A.A., Forbes P., Quinton A.R., Murphy A.R.J., Hipperson H., Daniell T.J., Ndeh D., Amsbury S., Hitchcock A., Lidbury I.D.E.A. Metabolism of hemicelluloses by root-associated bacteroidota species. ISME J. 2025;19:wraf022. doi: 10.1093/ismejo/wraf022. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Martínez J.L., Coque T.M., Baquero F. What is a resistance gene? Ranking risk in resistomes. Nat. Rev. Microbiol. 2015;13:116–123. doi: 10.1038/nrmicro3399. [ DOI ] [ PubMed ] [ Google Scholar ] McArthur A.G., Waglechner N., Nizam F., Yan A., Azad M.A., Baylay A.J., Bhullar K., Canova M.J., De Pascale G., Ejim L., Kalan L., King A.M., Koteva K., Morar M., Mulvey M.R., O’Brien J.S., Pawlowski A.C., Piddock L.J.V., Spanogiannopoulos P., Sutherland A.D., Tang I., Taylor P.L., Thaker M., Wang W., Yan M., Yu T., Wright G.D. The comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 2013;57:3348–3357. doi: 10.1128/AAC.00419-13. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Mende D.R., Waller A.S., Sunagawa S., Järvelin A.I., Chan M.M., Arumugam M., Raes J., Bork P. Assessment of metagenomic assembly using simulated next generation sequencing data. PLoS ONE. 2012;7 doi: 10.1371/journal.pone.0031386. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Munir N., Hanif M., Abideen Z., Sohail M., El-Keblawy A., Radicetti E., Mancinelli R., Haider G. Mechanisms and strategies of plant microbiome interactions to mitigate abiotic stresses. Agronomy. 2022;12:2069. doi: 10.3390/agronomy12092069. [ DOI ] [ Google Scholar ] Naitchede L.H.S., Ihearahu O.C., Saha K., Igwe D.O., Ray S., Ude G. Influence of triploid musa spp. Genome background and exogenous growth regulators on In vitro regeneration in plantains and bananas. Plants. 2025;14:2109. doi: 10.3390/plants14142109. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Navarro-Noya Y.E., Chávez-Romero Y., Hereira-Pacheco S., De León Lorenzana A.S., Govaerts B., Verhulst N., Dendooven L. Bacterial communities in the rhizosphere at different growth stages of maize cultivated in soil under conventional and conservation agricultural practices. Microbiol. Spectr. 2022;10 doi: 10.1128/spectrum.01834-21. -21. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Nayak S.K., Mishra B.B. 1st ed. CRC Press; 2020. Frontiers in Soil and Environmental Microbiology. [ DOI ] [ Google Scholar ] Neemisha, Sharma S. In: Structure and Functions of Pedosphere. Giri B., Kapoor R., Wu Q.-S., Varma A., editors. Springer Nature; Singapore, Singapore: 2022. Soil enzymes and their role in nutrient cycling; pp. 173–188. [ DOI ] [ Google Scholar ] Nie H., Zhou W., Zheng Z., Deng Y., Zhang W., Zhang M., Jiang Z., Zheng H., Yuan L., Yang J., Wang H. Exploring plant responses to altered gravity for advancing space agriculture. Plant Commun. 2025;6 doi: 10.1016/j.xplc.2025.101370. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Nielsen H.B., Almeida M., Juncker A.S., Rasmussen S., Li J., Sunagawa S., Plichta D.R., Gautier L., Pedersen A.G., Le Chatelier E., Pelletier E., Bonde I., Nielsen T., Manichanh C., Arumugam M., Batto J.-M., Quintanilha Dos Santos M.B., Blom N., Borruel N., Burgdorf K.S., Boumezbeur F., Casellas F., Doré J., Dworzynski P., Guarner F., Hansen T., Hildebrand F., Kaas R.S., Kennedy S., Kristiansen K., Kultima J.R., Léonard P., Levenez F., Lund O., Moumen B., Le Paslier D., Pons N., Pedersen O., Prifti E., Qin J., Raes J., Sørensen S., Tap J., Tims S., Ussery D.W., Yamada T., Renault P., Sicheritz-Ponten T., Bork P., Wang J., Brunak S., Ehrlich S.D. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 2014;32:822–828. doi: 10.1038/nbt.2939. [ DOI ] [ PubMed ] [ Google Scholar ] Ohlsson C., Lawenius L., Jiang Y., Horkeby K., Wu J., Nilsson K.H., Koskela A., Tuukkanen J., Movérare-Skrtic S., Henning P., Sjögren K. The beneficial effects of a probiotic mix on bone and lean mass are dependent on the diet in female mice. Sci. Rep. 2025;15:6182. doi: 10.1038/s41598-025-91056-2. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Olsen N.S., Riber L. Metagenomics as a transformative tool for antibiotic resistance surveillance: highlighting the impact of mobile genetic elements with a focus on the complex role of phages. Antibiotics. 2025;14:296. doi: 10.3390/antibiotics14030296. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ondov B.D., Bergman N.H., Phillippy A.M. Interactive metagenomic visualization in a web browser. BMC Bioinform. 2011;12:385. doi: 10.1186/1471-2105-12-385. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pasari N., Adlakha N., Gupta M., Bashir Z., Rajacharya G.H., Verma G., Munde M., Bhatnagar R., Yazdani S.S. Impact of Module-X2 and Carbohydrate Binding Module-3 on the catalytic activity of associated glycoside hydrolases towards plant biomass. Sci. Rep. 2017;7:3700. doi: 10.1038/s41598-017-03927-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pascale A., Proietti S., Pantelides I.S., Stringlis I.A. Modulation of the root microbiome by plant molecules: the basis for targeted disease suppression and plant growth promotion. Front. Plant Sci. 2020;10:1741. doi: 10.3389/fpls.2019.01741. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Pedrinho A., Mendes L.W., De Araujo Pereira A.P., Araujo A.S.F., Vaishnav A., Karpouzas D.G., Singh B.K. Soil microbial diversity plays an important role in resisting and restoring degraded ecosystems. Plant Soil. 2024;500:325–349. doi: 10.1007/s11104-024-06489-x. [ DOI ] [ Google Scholar ] Petrović M., Janakiev T., Grbić M.L., Unković N., Stević T., Vukićević S., Dimkić I. Insights into endophytic and rhizospheric bacteria of five sugar beet hybrids in terms of their diversity, plant-growth promoting, and biocontrol properties. Microb. Ecol. 2024;87:19. doi: 10.1007/s00248-023-02329-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Poon C. Factors implicating the validity and interpretation of mechanobiology studies in simulated microgravity environments. Eng. Rep. 2020;2 doi: 10.1002/eng2.12242. [ DOI ] [ Google Scholar ] Poupin M.J., Ledger T., Roselló-Móra R., González B. The Arabidopsis holobiont: a (re)source of insights to understand the amazing world of plant–microbe interactions. Environ. Microb. 2023;18:9. doi: 10.1186/s40793-023-00466-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Prasad B., Richter P., Vadakedath N., Haag F.W.M., Strauch S.M., Mancinelli R., Schwarzwälder A., Etcheparre E., Gaume N., Lebert M. How the space environment influences organisms: an astrobiological perspective and review. Int. J. Astrobiol. 2021;20:159–177. doi: 10.1017/S1473550421000057. [ DOI ] [ Google Scholar ] Priyadarshan P.M., Jain M., editors. Cash Crops: Genetic Diversity, Erosion, Conservation and Utilization. 1st ed. 2022. Springer International Publishing; Cham: 2022. [ DOI ] [ Google Scholar ] Qian K., Li D., Lin R., Shi Q., Mao Z., Yang Y., Feng D., Xie B. Rediscovery and analysis of Phytophthora carbohydrate esterase (CE) genes revealing their evolutionary diversity. J. Integr. Agric. 2018;17:878–891. doi: 10.1016/S2095-3119(17)61867-7. [ DOI ] [ Google Scholar ] Qin J., Li R., Raes J., Arumugam M., Burgdorf K.S., Manichanh C., Nielsen T., Pons N., Levenez F., Yamada T., Mende D.R., Li J., Xu J., Li Shaochuan, Li D., Cao J., Wang B., Liang H., Zheng H., Xie Y., Tap J., Lepage P., Bertalan M., Batto J.-M., Hansen T., Le Paslier D., Linneberg A., Nielsen H.B., Pelletier E., Renault P., Sicheritz-Ponten T., Turner K., Zhu H., Yu C., Li Shengting, Jian M., Zhou Y., Li Y., Zhang X., Li Songgang, Qin N., Yang H., Wang Jian, Brunak S., Doré J., Guarner F., Kristiansen K., Pedersen O., Parkhill J., Weissenbach J., Bork P., Ehrlich S.D., Wang Jun. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65. doi: 10.1038/nature08821. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Qin J., Li Y., Cai Z., Li, Shenghui, Zhu J., Zhang F., Liang S., Zhang W., Guan Y., Shen D., Peng Y., Zhang D., Jie Z., Wu W., Qin Y., Xue W., Li J., Han L., Lu D., Wu P., Dai Y., Sun X., Li Z., Tang A., Zhong S., Li X., Chen W., Xu R., Wang M., Feng Q., Gong M., Yu J., Zhang Y., Zhang M., Hansen T., Sanchez G., Raes J., Falony G., Okuda S., Almeida M., LeChatelier E., Renault P., Pons N., Batto J.-M., Zhang Z., Chen H., Yang R., Zheng W., Li, Songgang, Yang H., Wang, Jian, Ehrlich S.D., Nielsen R., Pedersen O., Kristiansen K., Wang, Jun A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. doi: 10.1038/nature11450. [ DOI ] [ PubMed ] [ Google Scholar ] Qin N., Yang F., Li A., Prifti E., Chen Yanfei, Shao L., Guo J., Le Chatelier E., Yao J., Wu L., Zhou J., Ni S., Liu L., Pons N., Batto J.M., Kennedy S.P., Leonard P., Yuan C., Ding W., Chen Yuanting, Hu X., Zheng B., Qian G., Xu W., Ehrlich S.D., Zheng S., Li L. Alterations of the human gut microbiome in liver cirrhosis. Nature. 2014;513:59–64. doi: 10.1038/nature13568. [ DOI ] [ PubMed ] [ Google Scholar ] Rajaseger G., Chan K.L., Yee Tan K., Ramasamy S., Khin M.C., Amaladoss A., Kadamb Haribhai P. Hydroponics: current trends in sustainable crop production. Bioinformation. 2023;19:925–938. doi: 10.6026/97320630019925. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Rawat P., Das S., Shankhdhar D., Shankhdhar S.C. Phosphate-solubilizing microorganisms: mechanism and their role in Phosphate solubilization and uptake. J. Soil Sci. Plant Nutr. 2021;21:49–68. doi: 10.1007/s42729-020-00342-7. [ DOI ] [ Google Scholar ] Raza A., Hafeez M.B., Zahra N., Shaukat K., Umbreen S., Tabassum J., Charagh S., Khan R.S.A., Hasanuzzaman M. In: The Plant Family Brassicaceae. Hasanuzzaman M., editor. Springer; Singapore, Singapore: 2020. The plant Family brassicaceae: introduction, biology, and importance; pp. 1–43. [ DOI ] [ Google Scholar ] Reda T., Thavarajah P., Polomski R., Bridges W., Shipe E., Thavarajah D. Reaching the highest shelf: a review of organic production, nutritional quality, and shelf life of kale (Brassica oleracea var. acephala) Plants People Planet. 2021;3:308–318. doi: 10.1002/ppp3.10183. [ DOI ] [ Google Scholar ] Refai M.Y. Identification of glycoside hydrolase CAZymes in rhizospheric soil microbiome of the wild plant species Moringa oleifera. Environ. Res. Commun. 2025;7 doi: 10.1088/2515-7620/adc87d. [ DOI ] [ Google Scholar ] Ribeiro D.O., Bonnardel F., Palma A.S., Carvalho A.L.M., Perez S. In: Carbohydrate Chemistry. Rauter P., Queneau A., Palma Y., Sá A., editors. Royal Society of Chemistry; 2024. CBMcarb-DB: interface of the three-dimensional landscape of carbohydrate-binding modules; pp. 1–22. [ DOI ] [ Google Scholar ] Riva V., Vergani L., Rashed A.A., El Saadi A., Sabatino R., Di Cesare A., Crotti E., Mapelli F., Borin S. Plant species influences the composition of root system microbiome and its antibiotic resistance profile in a constructed wetland receiving primary treated wastewater. Front. Microbiol. 2024;15 doi: 10.3389/fmicb.2024.1436122. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Rodriguez P.A., Rothballer M., Chowdhury S.P., Nussbaumer T., Gutjahr C., Falter-Braun P. Systems biology of plant-microbiome interactions. Mol Plant. 2019;12:804–821. doi: 10.1016/j.molp.2019.05.006. [ DOI ] [ PubMed ] [ Google Scholar ] Ruiz D., Céspedes-Bernal N., Vega A., Ledger T., González B., Poupin M.J. Nitrogen-modulated effects of the diazotrophic bacterium Cupriavidus taiwanensis on the non-nodulating plant Arabidopsis thaliana. Plant Soil. 2025;506:819–837. doi: 10.1007/s11104-024-06736-1. [ DOI ] [ Google Scholar ] Saak C.C., Dinh C.B., Dutton R.J. Experimental approaches to tracking mobile genetic elements in microbial communities. FEMS Microbiol. Rev. 2020;44:606–630. doi: 10.1093/femsre/fuaa025. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Šamec D., Urlić B., Salopek-Sondi B. Kale (Brassica oleracea var. acephala) as a superfood: review of the scientific evidence behind the statement. Crit. Rev. Food Sci. Nutr. 2019;59:2411–2422. doi: 10.1080/10408398.2018.1454400. [ DOI ] [ PubMed ] [ Google Scholar ] Sammauria R., Kumawat S., Kumawat P., Singh J., Jatwa T.K. Microbial inoculants: potential tool for sustainability of agricultural production systems. Arch. Microbiol. 2020;202:677–693. doi: 10.1007/s00203-019-01795-w. [ DOI ] [ PubMed ] [ Google Scholar ] Sangeetha J., Al-Tawaha A.R.M., Thangadurai D. 1st ed. Apple Academic Press; New York: 2026. Microbial Fertilizer Technology For Sustainable Crop Production. [ DOI ] [ Google Scholar ] Saral Sariyer A., Sariyer E. Vanillic acid’s potential to enhance AdeIJK, AdeFGH, and AdeABC efflux pump substrates in Acinetobacter baumannii. Biologia. 2025;80:1579–1587. doi: 10.1007/s11756-025-01925-4. [ DOI ] [ Google Scholar ] Satheesh N., Workneh Fanta S. Kale: review on nutritional composition, bio-active compounds, anti-nutritional factors, health beneficial properties and value-added products. Cogent Food Agric. 2020;6 doi: 10.1080/23311932.2020.1811048. [ DOI ] [ Google Scholar ] Schages L., Wichern F., Geisen S., Kalscheuer R., Bockmühl D. Distinct resistomes and microbial communities of soils, wastewater treatment plants and households suggest development of antibiotic resistances due to Distinct environmental conditions in each environment. Antibiotics. 2021;10:514. doi: 10.3390/antibiotics10050514. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Segata N., Izard J., Waldron L., Gevers D., Miropolsky L., Garrett W.S., Huttenhower C. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. doi: 10.1186/gb-2011-12-6-r60. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sharma G., Curtis P.D. The impacts of microgravity on bacterial metabolism. Life. 2022;12:774. doi: 10.3390/life12060774. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sharma M., Sudheer S., Usmani Z., Rani R., Gupta P. Deciphering the omics of plant-microbe interaction: perspectives and new insights. CG. 2020;21:343–362. doi: 10.2174/1389202921999200515140420. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Simmons T., Caddell D.F., Deng S., Coleman-Derr D. Exploring the root microbiome: extracting bacterial community data from the soil, rhizosphere, and root endosphere. JoVE. 2018;57561 doi: 10.3791/57561. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sonbol H.S., Jalal R.S. Functional profiling of abundant glycosyltransferases in the rhizospheric bacteriome of Abutilon fruticosum. Rhizosphere. 2025;33 doi: 10.1016/j.rhisph.2024.101001. [ DOI ] [ Google Scholar ] Su X., Guo Y., Fang T., Jiang X., Wang D., Li D., Bai P., Zhang B., Wang J., Liu C. Effects of simulated microgravity on the physiology of Stenotrophomonas maltophilia and multiomic analysis. Front. Microbiol. 2021;12 doi: 10.3389/fmicb.2021.701265. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Sun J., Lyons R., Weigh K.V., Lieske S., Newsham K.K., Hopkins D.W., Dennis P.G. Putative drivers of maritime Antarctic soil resistomes in the early 21st century: a baseline for monitoring environmental change and human influence. Sci. Total Environ. 2026;1014 doi: 10.1016/j.scitotenv.2026.181361. [ DOI ] [ PubMed ] [ Google Scholar ] Sunagawa S., Coelho L.P., Chaffron S., Kultima J.R., Labadie K., Salazar G., Djahanschiri B., Zeller G., Mende D.R., Alberti A., Cornejo-Castillo F.M., Costea P.I., Cruaud C., d’Ovidio F., Engelen S., Ferrera I., Gasol J.M., Guidi L., Hildebrand F., Kokoszka F., Lepoivre C., Lima-Mendez G., Poulain J., Poulos B.T., Royo-Llonch M., Sarmento H., Vieira-Silva S., Dimier C., Picheral M., Searson S., Kandels-Lewis S., Tara Oceans coordinators. Bowler C., De Vargas C., Gorsky G., Grimsley N., Hingamp P., Iudicone D., Jaillon O., Not F., Ogata H., Pesant S., Speich S., Stemmann L., Sullivan M.B., Weissenbach J., Wincker P., Karsenti E., Raes J., Acinas S.G., Bork P., Boss E., Bowler C., Follows M., Karp-Boss L., Krzic U., Reynaud E.G., Sardet C., Sieracki M., Velayoudon D. Structure and function of the global ocean microbiome. Science. 2015;348 doi: 10.1126/science.1261359. [ DOI ] [ PubMed ] [ Google Scholar ] Tan X.-Y., Liu X.-J., Lu D.-C., Ye Y.-Q., Liu X.-Y., Yu F., Yang H., Li F., Du Z.-J., Ye M.-Q. Insights into the physiological and metabolic features of thalassobacterium, a novel genus of verrucomicrobiota with the potential to drive the carbon cycle. mBio. 2025;16:e00305–e00325. doi: 10.1128/mbio.00305-25. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Tokuda M., Shintani M. Microbial evolution through horizontal gene transfer by mobile genetic elements. Microb. Biotechnol. 2024;17 doi: 10.1111/1751-7915.14408. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Toor M.D., Ur Rehman M., Abid J., Nath D., Ullah I., Basit A., Ud Din M.M., Mohamed H.I. Microbial ecosystems as guardians of food security and water resources in the era of climate change. Water Air Soil Pollut. 2024;235:741. doi: 10.1007/s11270-024-07533-3. [ DOI ] [ Google Scholar ] Trivedi P., Mattupalli C., Eversole K., Leach J.E. Enabling sustainable agriculture through understanding and enhancement of microbiomes. New Phytol. 2021;230:2129–2147. doi: 10.1111/nph.17319. [ DOI ] [ PubMed ] [ Google Scholar ] Ulbrich C., Wehland M., Pietsch J., Aleshcheva G., Wise P., Van Loon J., Magnusson N., Infanger M., Grosse J., Eilles C., Sundaresan A., Grimm D. The impact of simulated and real microgravity on bone cells and mesenchymal stem cells. Biomed. Res. Int. 2014;2014:1–15. doi: 10.1155/2014/928507. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Ullah F., Ali S., Siraj M., Akhtar M.S., Zaman W. Plant microbiomes alleviate abiotic stress-associated damage in crops and enhance climate-resilient agriculture. Plants. 2025;14:1890. doi: 10.3390/plants14121890. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Vandecraen J., Chandler M., Aertsen A., Van Houdt R. The impact of insertion sequences on bacterial genome plasticity and adaptability. Crit. Rev. Microbiol. 2017;43:709–730. doi: 10.1080/1040841X.2017.1303661. [ DOI ] [ PubMed ] [ Google Scholar ] Velazquez-Gonzalez R.S., Garcia-Garcia A.L., Ventura-Zapata E., Barceinas-Sanchez J.D.O., Sosa-Savedra J.C. A review on hydroponics and the technologies associated for medium- and small-scale operations. Agriculture. 2022;12:646. doi: 10.3390/agriculture12050646. [ DOI ] [ Google Scholar ] Villar E., Farrant G.K., Follows M., Garczarek L., Speich S., Audic S., Bittner L., Blanke B., Brum J.R., Brunet C., Casotti R., Chase A., Dolan J.R., d’Ortenzio F., Gattuso J.-P., Grima N., Guidi L., Hill C.N., Jahn O., Jamet J.-L., Le Goff H., Lepoivre C., Malviya S., Pelletier E., Romagnan J.-B., Roux S., Santini S., Scalco E., Schwenck S.M., Tanaka A., Testor P., Vannier T., Vincent F., Zingone A., Dimier C., Picheral M., Searson S., Kandels-Lewis S., Tara Oceans Coordinators. Acinas S.G., Bork P., Boss E., De Vargas C., Gorsky G., Ogata H., Pesant S., Sullivan M.B., Sunagawa S., Wincker P., Karsenti E., Bowler C., Not F., Hingamp P., Iudicone D. Environmental characteristics of Agulhas rings affect interocean plankton transport. Science. 2015;348 doi: 10.1126/science.1261447. [ DOI ] [ PubMed ] [ Google Scholar ] Wang D., Zhou X., Fu Q., Li Y., Ni B.-J., Liu X. Understanding bacterial ecology to combat antibiotic resistance dissemination. Trends Biotechnol. 2025;43:1566–1582. doi: 10.1016/j.tibtech.2024.12.011. [ DOI ] [ PubMed ] [ Google Scholar ] Wang H., Chen J., Du M., Ruan Y., Guo J., Shao R., Wang Y., Yang Q. In-depth insights into carbohydrate-active enzyme genes regarding the disparities in soil organic carbon after 12-year rotational cropping system field study. Eur. J. Soil Biol. 2024;123 doi: 10.1016/j.ejsobi.2024.103694. [ DOI ] [ Google Scholar ] Wang Q., Wang X., Sun S., Wang L., Sun Y., Guo X., Wang N., Chen B. Distribution characteristics of antibiotic resistance in direct-eating food and analysis of Citrobacter freundii genome and pathogenicity. WJE. 2024;21:1095–1107. doi: 10.1108/WJE-07-2023-0238. [ DOI ] [ Google Scholar ] Wassenaar T., Ussery D., Nielsen L., Ingmer H. Review and phylogenetic analysis of qac genes that reduce susceptibility to quaternary ammonium compounds in Staphylococcus species. Eur. J. Microbiol. Immunol. 2015;5:44–61. doi: 10.1556/EuJMI-D-14-00038. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Wedel E., Bernabe-Balas C., Ares-Arroyo M., Montero N., Santos-Lopez A., Mazel D., Gonzalez-Zorn B. Insertion sequences determine plasmid adaptation to new bacterial hosts. mBio. 2023;14 doi: 10.1128/mbio.03158-22. -22. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Weng J., Zhang Q., Wang B., Zhang C., Zhang H., Meng J. Detecting walnut leaf scorch using UAV-based hyperspectral data, genetic algorithm, random forest and support vector machine learning algorithms. Remote Sens. (Basel) 2025;17:3986. doi: 10.3390/rs17243986. [ DOI ] [ Google Scholar ] Wheeler R.M. Agriculture for space: people and places paving the way. Open Agric. 2017;2:14–32. doi: 10.1515/opag-2017-0002. [ DOI ] [ Google Scholar ] Wu Z., Liu X., Liu H., Hu D., Degermendzhi A., Bartsev S., Fu Y. Microgravity’s grip: transforming plant-microbe interactions for space sustainability. Acta Astronaut. 2025;231:80–92. doi: 10.1016/j.actaastro.2025.02.032. [ DOI ] [ Google Scholar ] Xiao, L., Zhao, Q., Deng, J., Cui, L., Zhang, T., Yang, Q., Zhao, S., 2025. Comparative analysis of rhizosphere microbiomes in different blueberry cultivars. Horticulturae 11, 696. 10.3390/horticulturae11060696. [ DOI ] Xing Y., Wang X., Mustafa A. Exploring the link between soil health and crop productivity. Ecotoxicol. Environ. Saf. 2025;289 doi: 10.1016/j.ecoenv.2025.117703. [ DOI ] [ PubMed ] [ Google Scholar ] Yan H.-K., Zhang C.-C., Nai G.-J., Ma L., Lai Y., Pu Z.-H., Ma S.-Y., Li S. Microbial inoculant GB03 increased the yield and quality of grapefruit under salt-alkali stress by changing rhizosphere Microbial communities. Foods. 2025;14:711. doi: 10.3390/foods14050711. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zaheer R., Noyes N., Ortega Polo R., Cook S.R., Marinier E., Van Domselaar G., Belk K.E., Morley P.S., McAllister T.A. Impact of sequencing depth on the characterization of the microbiome and resistome. Sci. Rep. 2018;8:5890. doi: 10.1038/s41598-018-24280-8. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zeller G., Tap J., Voigt A.Y., Sunagawa S., Kultima J.R., Costea P.I., Amiot A., Böhm J., Brunetti F., Habermann N., Hercog R., Koch M., Luciani A., Mende D.R., Schneider M.A., Schrotz-King P., Tournigand C., Tran Van Nhieu J., Yamada T., Zimmermann J., Benes V., Kloor M., Ulrich C.M., Von Knebel Doeberitz M., Sobhani I., Bork P. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 2014;10:766. doi: 10.15252/msb.20145645. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang C., Li Z., Miao Y., Liao X. Combining effects of nitrogen fertilizer and biochar on soil N2O emissions and microbial community in a subtropical rapeseed-soybean rotation. Eur. J. Soil Biol. 2025;126 doi: 10.1016/j.ejsobi.2025.103750. [ DOI ] [ Google Scholar ] Zhang J., Lei H., Huang J., Wong J.W.C., Li B. Co-occurrence and co-expression of antibiotic, biocide, and metal resistance genes with mobile genetic elements in microbial communities subjected to long-term antibiotic pressure: novel insights from metagenomics and metatranscriptomics. J. Hazard. Mater. 2025;489 doi: 10.1016/j.jhazmat.2025.137559. [ DOI ] [ PubMed ] [ Google Scholar ] Zhang J., Tang A., Jin T., Sun D., Guo F., Lei H., Lin L., Shu W., Yu P., Li X., Li B. A panoramic view of the virosphere in three wastewater treatment plants by integrating viral-like particle-concentrated and traditional non-concentrated metagenomic approaches. iMeta. 2024;3:e188. doi: 10.1002/imt2.188. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang K., Shi Y., Lu H., He M., Huang W., Siemann E. Soil bacterial communities and co-occurrence changes associated with multi-nutrient cycling under rice-wheat rotation reclamation in coastal wetland. Ecol. Indic. 2022;144 doi: 10.1016/j.ecolind.2022.109485. [ DOI ] [ Google Scholar ] Zhang M., Yin Z., Chen B., Yu Z., Liang J., Tian X., Li D., Deng X., Peng L. Investigation of Citrobacter freundii clinical isolates in a Chinese hospital during 2020–2022 revealed genomic characterization of an extremely drug-resistant C. freundii ST257 clinical strain GMU8049 co-carrying blaNDM-1 and a novel blaCMY variant. Microbiol. Spectr. 2024;12 doi: 10.1128/spectrum.04254-23. -23. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang P., Mao D., Gao H., Zheng L., Chen Z., Gao Y., Duan Y., Guo J., Luo Y., Ren H. Colonization of gut microbiota by plasmid-carrying bacteria is facilitated by evolutionary adaptation to antibiotic treatment. ISME J. 2022;16:1284–1293. doi: 10.1038/s41396-021-01171-x. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhang, W., Chang, L., Cao, Y., Wang, S., Lyu, C., Kang, C., Zhou, L., Huang, L., Guo, L., 2024c. Glycosylation of plant secondary metabolites: the regulation from chaos to harmony. 10.22541/au.170665539.90584931/v1. [ DOI ] Zhou D., Li G., Qin S.J. Total projection to latent structures for process monitoring. AIChE J. 2010;56:168–178. doi: 10.1002/aic.11977. [ DOI ] [ Google Scholar ] Zhou W., Chen Q., Qian C., Shen K., Zhu X., Zhou D., Lu W., Sun Z., Liu H., Li K., Xu T., Bao Q., Lu J. In vitro susceptibility and florfenicol resistance in Citrobacter isolates and whole-genome analysis of multidrug-resistant Citrobacter freundii. Int. J. Genomics. 2019;2019:1–15. doi: 10.1155/2019/7191935. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Zhu W., Lomsadze A., Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010;38:e132. doi: 10.1093/nar/gkq275. –e132. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ] Associated Data This section collects any data citations, data availability statements, or supplementary materials included in this article. Supplementary Materials mmc1.docx (926.3KB, docx) mmc2.xls (23KB, xls) mmc3.xls (22KB, xls) mmc4.xls (27.5KB, xls) mmc5.xls (444.5KB, xls) mmc6.xls (21.5KB, xls) mmc7.xls (328.5KB, xls) mmc8.xls (466.5KB, xls) mmc9.xls (1.9MB, xls) mmc10.xls (27.5KB, xls) Supplementary materials mmc11.xls (26KB, xls) Data Availability Statement Data will be made available on request. Articles from Current Research in Microbial Sciences are provided here courtesy of Elsevier ACTIONS View on publisher site PDF (17.5 MB) Cite Collections Permalink PERMALINK Copy RESOURCES Similar articles Cited by other articles Links to NCBI Databases Cite Copy Download .nbib .nbib Format: AMA APA MLA NLM Add to Collections Create a new collection Add to an existing collection Name your collection * Choose a collection Unable to load your collection due to an error Please try again Add Cancel Follow NCBI NCBI on X (formerly known as Twitter) NCBI on Facebook NCBI on LinkedIn NCBI on GitHub NCBI RSS feed Connect with NLM NLM on X (formerly known as Twitter) NLM on Facebook NLM on YouTube National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894 Web Policies FOIA HHS Vulnerability Disclosure Help Accessibility Careers NLM NIH HHS USA.gov Back to Top