Seed Microbiota: A Key Factor in Plant Adaptation to Arsenic Stress ​

Seed Microbiota: A Key Factor in Plant Adaptation to Arsenic Stress ​ | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seed Microbiota: A Key Factor in Plant Adaptation to Arsenic Stress ​ Natalia Gonzalez-Benitez, Maria del Carmen Molina, Stephan Pollmann, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9048400/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Aim: Arsenic contamination represents one of the most critical anthropogenic stressors compromising organism resilience in the context of Global Change. However, some plant species can complete their life cycle in soils contaminated with this metalloid. Studies on plant–soil microbiome symbiosis have emphasized horizontal microbiome transmission (from soil to roots), while underestimating the role of vertically transmitted seed borne microbiomes. This work examines the seed borne endophytic bacterium Acinetobacter radioresistens MC 14, isolated from arsenic hyper resistant plants Jasione montana and known for its arsenic tolerance and plant growth promoting traits. The study investigates its capacity to modulate plant phenotypic traits and enhance adaptation under arsenic stress. Methods: To this end, we evaluated the physiological responses of Arabidopsis thaliana exposed to As(III) concentrations following inoculation with A. radioresistens MC 14, which apoplastically colonizes roots and establishes a non invasive facultative symbiosis that improves plant survival under arsenic stress. Results: A. radioresistens MC 14 improves plant fitness and ecological success, with optimal inoculum levels maximizing the benefits of the interaction while minimizing symbiotic costs. A. radioresistens MC 14 mitigates arsenic induced phytohormonal imbalances in roots during early development. This bacterium–plant association promotes root growth and reduces As(III) triggered oxidative stress by activating cellular recovery mechanisms. As a result, plants produce more roots, flowers, and leaves even under toxic conditions. Conclusions: These findings indicate that plants exert selective pressure on their seed microbiome, driving co evolution and maintaining beneficial microbial reservoirs across generations, ultimately enhancing plant performance in stressful environments. seed endophyte arsenite plant growth promoting bacteria metalloid resistance Acinetobacter radioresistens MC 14 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Highlights Seed‑borne endophytes represent a potential reservoir that supports plant survival under environmental stress. Seed‑borne A. radioresistens MC 14 apoplastically colonizes non‑native roots, forming a non‑invasive facultative symbiosis supported by key genomic traits that enable rhizosphere adaptation and plant association. A. radioresistens MC 14 carries key genes that facilitate root colonization and rhizosphere adaptation, providing strong bioremediation potential. Under high arsenic stress, seed‑borne A. radioresistens MC 14 improves plant fitness, stress tolerance, and successful progression into reproductive stages. Introduction Pollution, climate change, and biodiversity loss are among the most critical global environmental challenges, with pollution alone contributing to approximately nine million deaths annually—one in six worldwide (Fuller et al., 2022 ). Plant ecosystems, essential to both natural and agricultural productivity, are increasingly affected by these stressors. Abiotic factors such as arsenic contamination, alongside biotic pressures, directly impair plant development and biodiversity, posing a serious threat to global food security (Zandalinas et al., 2021 ). Current projections for food production often neglect the compounded effects of climate change and arsenic contamination, which reduce crop yields and increase arsenic accumulation in seeds (Muehe et al., 2019 ). This issue is particularly severe in rice, a staple for over 350 million people in developing regions, due to its high capacity for arsenic bioaccumulation (Geng et al., 2023 ). Globally, arsenic contamination affects more than 140 million individuals across aquatic and terrestrial systems, frequently exceeding World Health Organization (WHO) safety thresholds (Babar & Tariq, 2018 ). In response to such environmental pressures, plants have evolved various acclimation strategies to withstand adverse conditions. Over recent decades, evidence has highlighted the pivotal role of plant-microbe interactions, especially in the rhizosphere, in enhancing resilience to abiotic stress (Berendsen et al., 2012 ). More recently, the endosphere has gained attention for its contribution to plant adaptability. In this context, seed endomicrobiota has a dual origin: horizontal transmission, mainly from soil and vertical transmission via maternal inheritance through seeds. Despite their biological importance, seed microbiomes—particularly in wild plants—have often been overlooked, despite exhibiting high taxonomic diversity throughout their life cycle (Chesneau et al., 2022 ). Although the environmental and biological stressors that shape the seed microbiota remain insufficiently characterized, the genetic composition of seed microbiomes is increasingly recognized as a key factor in modulating plant responses to environmental fluctuations and influencing the evolutionary trajectory of the plant metaorganism (Abdullaeva et al., 2021 ; González-Benítez et al., 2021 ; Matsumoto et al., 2021; Nelson, 2018 ; Wallace, 2023 ). Ecological selection has recently been identified as the principal mechanism influencing the succession of dominant taxa during the process of seed filling and maturation (Chesneau et al., 2022 ; Nelson, 2018 ). Although plant-bacterial interactions are increasingly recognised for enhancing resilience under arsenic stress (Molina et al., 2019 ; Singh et al., 2023 ), the role of maternally inherited seed microbiota in detoxification, adaptation, and evolution remains largely unexplored. Preliminary evidence suggests their importance; for example, Rhodococcus rhodochrous , a seed endophyte from Jasione montana , can modulate the phenotypic responses of arsenic-sensitive J. sessiliflora , conferring enhanced tolerance under arsenic exposure (González-Benítez et al., 2021 ). Sequencing the genomes of such endophytes offers a novel approach to understanding their functional potential, often overlooked in seed physiology studies. This strategy enables the identification of beneficial genes, their interactions with the plant genome, and mechanisms of stress resilience. It also supports biofertilizer development and biotechnological innovations for bioremediation. Among seed-associated bacteria, Acinetobacter species—frequently found in ovules and seeds (Verma and White, 2019 )—have shown notable arsenic-transforming capabilities. Certain strains can oxidize over 80% of As(III) to As(V), linked to the presence of the aioA gene (Basu et al., 2023 ; Dutta et al., 2023 ). A. radioresistens , in particular, stands out for its phenotypic diversity, including plant growth promotion (Lafi et al., 2016 ; Zapata-Sifuentes et al., 2022 ), pollutant degradation (Cattuci et al., 2025; Hidalgo et al., 2025 ; Liu et al., 2020; Macaya et al., 2019 ; Xiang et al., 2023 ), and extremotolerance (McCoy et al., 2012 ), largely attributed to its genomic plasticity and mobile genetic elements (Gentilini et al., 2018 ; Walter et al., 2020 ). The MC-14 strain of A. radioresistens , isolated from J. montana in in an arsenic-contaminated area (García-Salgado et al., 2012 ), is cultivable under standard conditions and exhibits arsenate resistance (MIC = 200 mM), antifungal activity, and plant growth-promoting traits (Molina et al., 2019 ). These features make it an ideal model for exploring how seed microbiomes contribute to plant fitness and adaptation under arsenic stress, and for investigating the functional potential of the plant hologenome. In this study, we propose the following objectives: (1) to perform a complete genome sequencing of the A. radioresistens MC-14 strain, with a particular emphasis on identifying genes implicated in detoxification and metabolic pathways related to arsenic and heavy metal stress, the presence of mobile genetic elements involved in horizontal gene transfer, and plant interaction genes; and (2) to assess and quantify the beneficial effects of this seed-derived strain on plant fitness and adaptation using Arabidopsis thaliana as a model host species for inoculation experiments. Genomic characterization will shed light on the functional contribution of maternally inherited microbiota to plant physiological adaptation under arsenic stress. This knowledge could inform the application of such bacteria in phytostimulation, and detoxification strategies, ultimately contributing to more sustainable agricultural practices and improved food quality. Material and methods Biological materials, culture media and growth conditions Acinetobacter radioresistens MC-14 was previously isolated from seeds of Jasione montana plants (Molina et al., 2019 ), a highly arsenic tolerant plant species (Benson et al., 1981 ), collected from the vicinity of the Monica mine (Bustarviejo, Madrid) an area characterized by arsenic-contaminated soils with concentrations ranging from 0.3 to 30 g·kg⁻¹ (García-Salgado et al., 2012 ). For whole-genome sequencing, A. radioresistens MC-14 was cultivated aerobically in LB medium at 30°C. Upon obtaining sufficient mass, genomic DNA was extracted and sequenced by MicrobesNG company (Birmingham, U.K) following a combination of Nanopore long-read sequencing (Clarke et al., 2009 ) and Illumina short-read sequencing (250-nt paired-end reads) to refine the genome sequence. To assess resistance of A. radioresistens MC-14 to metals and metalloids, bacterial cultures were grown in sodium As(III) (NaAsO₂; 0–40 mM; Merck), as well as nickel chloride (NiCl₂), chromium(II) chloride (CrCl₂), cobalt(II) sulfate (CoSO₄), copper(II) sulfate (CuSO₄), lead(II) nitrate [Pb(NO₃)₂], and zinc sulfate (ZnSO₄) at concentrations ranging from 0 to 5 mM (Sigma-Aldrich). After 24 hours of incubation at 30°C (end of exponential growth phase), bacterial growth was evaluated spectrophotometrically by measuring optical density at 600 nm (OD₆₀₀nm). For bacterial inoculation experiments, A. radioresistens MC-14 was cultivated in LB broth until reaching OD 600nm of 0.07, 0.21, and 0.63 at 600 nm, as measured with a Spectronic Genesys-8 spectrophotometer (Spectronic Instruments Inc.). After sterilization and cooling of 50 mL of MS broth medium, 0.1 mL of the bacterial suspension at the desired concentration was added to the growth plates. Although the A. radioresistens MC-14 strain originates from J. montana , the functional assays were conducted in A. thaliana , the reference model in plant biology due to its fully sequenced genome, the availability of numerous mutant and transgenic lines, and standardized protocols for microscopic and genetic analyses (Ortega-Villaizán et al., 2024). Arabidopsis thaliana seeds of the wild-type accession Columbia-0 (Col-0, stock N1092) were obtained from the Nothingham Arabidopsis Stock Centre (NASC). A. thaliana was grown on sterile 0.5⋅ Murashige & Skoog (MS) medium plates (Murashige & Skoog, 1962 ) supplemented with 0.5 g of MES monohydrate L − 1 of medium and 1% of agar (w/v). The pH was adjusted to 5.8 using KOH. To sterilize A. thaliana seeds, they were first agitated in 70% ethanol for 2 minutes, followed by immersion in a sterilization solution containing 1% sodium dodecyl sulfate (w/v) and 2% sodium hypochlorite (w/v) in sterile distilled water for 12 min. The seeds were then washed five times with sterile MilliQ water. To confirm complete surface sterilization, the seeds were plated on Luria Bertani (LB) agar (Condalab). After 24 hours, the absence of bacterial growth confirmed sterilization. Molecular and genomic, approaches to characterize A. radioresistens MC-14 A. radioresistens MC-14 was transformed with the pBAV1k-t5-gfp plasmid (Bryksin and Matsumura, 2010 ), which conferred the ability to produce a green fluorescent protein (GFP) and harbors kanamycin resistance. GFP expressing A. radioresistens MC-14 cells were grown at 28°C in LB medium supplemented with kanamycin at a concentration of 50 µgm − 1 . Plant Transformation and Co-Cultivation Conditions. Arabidopsis plants were transformed using Agrobacterium tumefaciens harboring the NPTII gene conferring resistance to kanamycin, following the floral dip method described by Clough and Bent ( 1998 ). The antibiotic kanamycin was included in the growth medium to prevent the loss of the GFP-encoding plasmid in A. radioresistens MC-14 during plant-microbe co-cultivation assays. Gene prediction, genome annotation and sequence data analyses. The genome of A. radioresistens MC-14 was annotated using the Rapid Annotation using Subsystem Technology (RAST) pipeline (Overbeek et al., 2008). Functional annotation of predicted protein-coding genes was performed by comparison against several curated protein databases, including SwissProt, NCBI Protein, Clusters of Orthologous Groups (COG), Pfam, and SMART, using BLAST and RPS-BLAST algorithms (Altschul et al., 1997 ). Pairwise and multiple sequence alignments of proteins were conducted using the ClustalW algorithm (Thompson et al., 1994 ) via the EMBL-EBI server. Phylogenetic analyses were performed using the Kimura two-parameter model (Kimura, 1980 ), and evolutionary trees were constructed with the neighbor-joining method (Saitou and Nei, 1987 ) implemented in the PHYLIP software package (Felsenstein, 1993 ). Comparative genomic analyses were conducted using Geneious software, version 10.0.2 ( https://www.geneious.com/ ), and Venn diagrams were generated with EDGAR (Blom et al., 2009 ). The complete genome sequence and corresponding annotation of A. radioresistens MC-14 have been submitted to the NCBI GenBank database under accession number CP131483. Effects of arsenic on A. thaliana colonization by A. radioresistens To evaluate the impact of arsenic [As(III)] exposure and A. radioresistens MC-14 inoculation on A. thaliana phenotypic responses, a full-factorial experimental design was employed. The experiment comprised four arsenic concentrations (0, 3, 5, and 10 µM) and four bacterial inoculation levels, defined at OD₆₀₀ (0, 0.07, 0.21, or 0.63). Each treatment included three replicate plates, with six plants per plate (18 plants per treatment), resulting in a total of 24 plates (4×2×3), and a total of 144 plants. Surface-sterilized A. thaliana seeds were sown on 0.5x MS plates and stratified in the dark at 4°C for two days. Thereafter, plants were transferred to treatment media containing either 0 (control), 3, 5, or 10 µM As(III), along with varying concentrations of A. radioresistens MC-14 (OD₆₀₀ = 0, 0.07, 0.21, or 0.63), depending on the specific experimental condition. Florescence and gas exchange parameters were measured at the end of the cultivation period (30 days) in 4 plants per plate (12 plants per treatment). Measurements were conducted between 7:00 and 10:00 h (solar time) using an infrared gas analyzer (LICOR 6400XT, Li-Cor Inc., Lincoln NE, USA). Environmental conditions were adjusted to match those of the growth chamber; air temperature inside the cuvette was set to 20°C, CO 2 concentration to 400 µmol mol − 1 , vapor pressure deficit to 1.5, and photosynthetic photon flux density (PPD) to 800 µmol m −2 s − 1 . Initial measurements were performed under dark conditions (PPFD = 0). Once the net photosynthetic rate (Aₙₑₜ) stabilized, the maximum potential quantum efficiency of Photosystem II (Fv/Fm) and dark respiration rate (A dark ) were recorded. Subsequently, light was applied, and after a minimum of 10 min, to allow the plants for light acclimation, one Anet stabilized again, the following parameters were measured: effective quantum yield of PSII (Φ PSII ), photochemical quenching (qP), non-photochemical quenching (qN), and Anet. The phenological stage of each plant was also recorded based on the following scale: 0, no visible inflorescence; 1, inflorescence present but peduncle not elongated; 2, elongated peduncle with discernible flower buds; 3, first flower bud opened, but most of them remained closed; 4, all flower buds open, with onset of wilting and abscission of stamens and petals. Additionally, plant survival was recorded for all individuals. A plant was considered dead when all leaves had turned brown and no signs of new growth were observed for a period of at least five consecutive days. Then, plants were harvested and divided into leaves, inflorescence and roots. Leaf samples were immediately frozen at − 30°C and stored until analysis of photosynthetic pigment concentrations. Pigment quantification was performed on the same plants used for gas exchange measurements. Leaf subsamples (20 mg fresh weight) were extracted using pure dimethyl sulfoxide (DMSO) to determine the concentrations of chlorophyll a (cla), chlorophyll b (clb) and total carotenoids (xanthophylls + carotenoids; cars), following methodology described in Barnes et al. ( 1992 ). Pigment concentrations were calculated using the equations provided by Wellburn (1994). All pigment values were expressed on a dry mass per area basis. Finally, all plant organs (leaves, inflorescences, and roots) were oven-dried and weighed to determine dry biomass. Bacterial colonization of plants. To visualize and localize A. radioresistens within root tissues, at least five plants of transgenic A. thaliana containing the NPII gene inoculated with A. radioresistens MC-14 harboring the plasmid pBAV1k-t5-gfp for GFP expression. Plants were grown under controlled conditions in a growth chamber with a 16 h light / 8 h dark photoperiod at 22/18°C (light/dark), and a photon flux density of 400 µmol m⁻² s⁻¹. Visualization was performed at the post-germination stage, five days after sowing. Visualization was conducted using a Zeiss LSM 880 confocal laser microscope with excitation using an Argon multiline laser at 488 nm and a 494/596 nm broadband filter for GFP detection. Differential interference contrast (DIC) imaging was applied, and observations were processed using the maximum intensity projection technique to enhance spatial resolution. Statistical analysis. All analyses were performed using R version 4.3.0 (R Core Team, 2023). The fixed effects of A. radioresistens MC-14 inoculation (qualitative) and As(III) concentration (quantitative) were analyzed using linear mixed models including plate as a random factor. The influence of A. radioresistens MC-14 inoculation and As(III) concentration on plant phenological stage was evaluated using an ordinal multinomial analysis with the function polr from the “ MASS ” package (Venables and Ripley, 2002 ). Results Whole-genome analysis of A. radioresistens MC-14 The complete genome of A. radioresistens MC-14 was sequenced using a hybrid approach combining Illumina and Oxford Nanopore technologies, as conducted by the MicrobesNG (Birmingham, UK) genome sequencing service. The Sequencing generated a total of 319,201 reads, resulting in approximately 30 × genome coverage. The assembled genome consisted of a single circular chromosome with a predicted length of 3,161,054 base pairs, along with three plasmids measuring 21,734 bp (plasmid 1), 7,990 bp (plasmid 2), and 4,507 bp (plasmid 3), respectively (Fig. 1 ). Genome annotation predicted a total of 3,000 coding sequences and 98 RNA genes, including 77 tRNA and 21 rRNA genes. Whole-genome comparison of A. radioresistens MC-14 with other Acinetobacter radioresistens strains revealed high average nucleotide identity (ANI) values: 99.64% with strain FDAARGOS_731 (GCA_014069095; 63.5% genome alignment), 98.47% with DSM 6976 (GCA_000248115; 63.55% aligned), 98.58% with LH6 (CP030031; 62.99% aligned), 98.57% with DD78 (65.84% aligned), and 98.62% with DSSKY-A001 (74.29% aligned). In contrast, comparisons with other Acinetobacter species such as A. baylyi ADP1 and A. variabilis RYU24 resulted in significantly lower ANI values of 73.95% (39.97% aligned) and 74.91% (38.94% aligned), respectively. Analysis of the 16S rRNA gene sequence confirmed the close phylogenetic relationship between A. radioresistens MC-14 and other strains of the species, showing 100% identity with FDAARGOS_731, and > 99.7% identity with DSM 6976, DSSKY-A001, LH6, and DD18 (Figure S1). Notably, the GC content of the three plasmids identified in the MC-14 genome was markedly lower than that of the chromosome: 37.5% (plasmid 1), 34.1% (plasmid 2), and 31.6% (plasmid 3) (Table 1 ). Table 1 Genome features of A. radioresistens MC-14 in comparison to those of strains FDAARGOS_731 and DSM 6976. Genome features MC-14 FDAARGOS_731 DSM 6976 T Origin A. thaliana seed human skin oil soil Total size (bp) 3161054 3377444 3433938 GC% chromosome (bp) 41.7 (3126823) 41.7 (3218180) 41.8 (3038795) GC% plasmid 1 (bp) 37.5 (21734) 37.5 (58767) 37.1 (240231) GC% plasmid 2 (bp) 34.1 (7990) 37.1 (9483) 39.2 (77605) GC% plasmid 3 (bp) 31.6 (4597) 34.9 (27601) 42.7 (29910) GC% plasmid 4 (bp) -- 39.4 (61168) 36.2 (22527) GC% plasmid 5 (bp) -- 39.1 (2245) 36.3 (9388) GC% plasmid 6 (bp) -- -- 35.3 (5898) GC% plasmid 7 (bp) -- -- 39.5 (5208) GC% plasmid 8 (bp) -- -- 32.7 (4376) Proteins 3000 3302 3330 RNA 96 97 81 Plasmids 3 5 8 The A. radioresistens strains included in the comparison originate from diverse habitats. Strain MC-14 was isolated from J. montana seeds collected from arsenic-contaminated soils; FDAARGOS_731 from human skin (Roca et al., 2024 ); LH6 from the avian intestinal tract (Crispen et al., 2018); DSM 6976 from cotton and gamma-sterilized soil (Nishimura et al., 1988); and DD78 from oil-contaminated sludge from the delta of the Aconcagua River. Ortholog analysis was performed to compare the genome of MC-14 with the closest strains. A Venn diagram (Fig. 2 ) illustrates the core and pan-genome composition of A. radioresistens . A total of 2,465 genes were shared among the four analyzed strains. MC-14 shared 2,474 genes with DD18, 2,654 with FDAARGOS_731, and 2,472 with LH6. Additionally, 202 genes were identified as unique singletons exclusive to the MC-14 genome (Fig. 2 ). Genomic insights into heavy metal and arsenic resistance in A. radioresistens MC-14. The genomic analysis of A. radioresistens MC-14 revealed a substantial number of genes encoding proteins that may confer resistance to heavy metals and metalloids (Table S1). Minimum inhibitory concentration (MIC) assays indicated tolerance levels of 2.5 mM for CoSO₄ and NiCl₂, > 5 mM for CrCl₂, 5 mM for Pb(NO₃)₂ and ZnSO₄, and 1 mM for CuSO₄ (Fig. 3 ). Remarkably, A. radioresistens MC-14 strain demonstrated substantial resistance to As(III), with growth sustained up to 20 mM and observable even at 25 and 30 mM concentrations (Fig. 4 ). Genome annotation identified two distinct chromosomal gene clusters encoding arsenic resistance proteins (Fig. 5 ). Cluster 1 consists of the arsHB1C1R1C2 operon, while cluster 2 contains arsB2R2C3 . Both clusters are located on the chromosome. Comparative analysis with the closely related strains DSM6976 and FDAARGOS_731 revealed conserved ars operon architecture; however, the genomic positioning of these clusters varies. In DSM6976, a gene cluster analogous that resembles cluster 2 of A. radioresistens MC-14 is chromosomally located, whereas a cluster resembling MC-14 strain’s cluster 1 is plasmid-encoded. A similar genomic arrangement was observed for FDAARGOS_731, with cluster 2-like genes chromosomally located and cluster 1-like genes present on a plasmid (Fig. 5 ). Interestingly, protein sequence comparisons revealed that the ArsR2 protein from MC-14 cluster 2 shares a higher degree of sequence identity with homologs encoded on plasmid-borne ars operons of DSM6976 and FDAARGOS_731 than with their chromosomal counterparts (Table S2). Phylogenetic analysis further supports this, showing that ArsR2 and other proteins encoded by A. radioresistens MC-14 cluster 2 group closely with plasmid-derived ars proteins from these related strains (Fig. 6 ). Notably, plasmid 2 of DSM6976 and plasmid 1 of FDAARGOS_731 harbor not only genes related to resistance to heavy metals and metalloids but also numerous genes associated with horizontal gene transfer, suggesting that these ars operons may have been acquired via mobile genetic elements. Genomic determinants of an endophytic lifestyle . After ten days of seedling growth, A. radioresistens MC-14 established a robust biofilm on the root surface, which was easily observable (Figure S2). Detailed microscopic examination further demonstrated that the bacteria were predominantly located within the apoplastic compartment, occupying intercellular spaces without penetrating plant cells, thus confirming their extracellular position within the root tissue. Notably, no evidence of intracellular was observed, indicating that A. radioresistens MC-14 adopts a non-invasive strategy, residing exclusively on the root surface and within intercellular spaces (Fig. 7 ). We conducted a comprehensive analysis of the A. radioresistens MC-14 genome to identify genes potentially associated with traits relevant to plant-associated lifestyles. Genes related to bacterial motility were organized into distinct clusters encoding components of type IV pili and the chemotaxis apparatus (Table S3). The genome also harbors loci putatively involved in exopolysaccharide biosynthesis (Table S3), which are commonly implicated in biofilm formation and host interaction. Furthermore, we identified genes encoding enzymes such as catalases, superoxide dismutases, and peroxidases (Table S3), which are likely involved in reactive oxygen species (ROS) detoxification, a key feature for successful colonization of plant tissues. Genes orthologous to known osmolyte transporters, including those for choline and betaine uptake, were also present (Table S3), suggesting potential mechanisms for osmoregulation under plant-associated conditions. Notably, the genome encodes putative components of multiple secretion systems (types I, II, III, and VI) (Table S3). These systems are frequently associated with effector delivery, immune suppression, and promotion of intracellular colonization in plant hosts. The presence of gene clusters previously associated with plant-microbe interactions supports the hypothesis that A. radioresistens MC-14 possesses genetic traits consistent with an endophytic lifestyle. Plant growth promoting effects under As(III)-induced stress A. thaliana exhibited full survival in control conditions lacking As(III), irrespective of bacterial inoculum concentration. In contrast, non-inoculated plants showed a marked decrease in survival at As(III) concentrations ≥ 5 µM, with only 36% of plants surviving exposure to 10 µM As(III). Notably, plant survival under As(III) stress was significantly influenced by the inoculation dose (interaction As(III) × inoculation F = 11.99; p < 0.001). Inoculation a low cell density (OD₆₀₀ = 0.07) did not confer any protective effect; however, higher inoculum densities (OD₆₀₀ = 0.21 or 0.63) resulted in complete plant survival even at the highest As(III) concentration tested (10 µM), highlighting a dose-dependent protective effect of A. radioresistens MC-14. The phenological progression of A. thaliana under As(III) exposure was significantly influenced by the bacterial inoculum dose (χ² = 165, p < 0.001; Fig. 8 ). In the absence of As(III), bacterial inoculation induced a slight delay in the development of the plants. Increasing As(III) concentrations markedly reduced the probability of plants reaching advanced flowering stages, with complete inhibition of reproductive development observed at the highest As(III) concentration. However, plants inoculated with A. radioresistens MC-14 at OD₆₀₀ values of 0.21 or 0.63 demonstrated significantly improved progression to later phenological stages, even under the most severe As(III) stress. In absence of As(III) in the medium, inoculation did not result in significant differences in organ or plant mass. With increasing As(III) concentrations, a progressive reduction in both organ-specific and total biomass was observed. Importantly, these effects, particularly on inflorescence and root biomass, were significantly dependent on the inoculation dose (As(III) × inoculation interaction, p < 0.021 for all cases; Fig. 9 ). While low As(III) levels (e.g., 3 µM) elicited minimal growth promotion, increasing the bacterial inoculum enhanced plant growth at higher As(III) concentrations, with maximal biomass observed at OD₆₀₀ = 0.21. Net photosynthetic rate (Aₙₑₜ) increased with rising As(III) concentrations, but this effect was restricted to non-inoculated seedlings (As(III) × inoculation interaction, Table 2 ). In contrast, dark respiration exhibited a positive correlation with As(III) concentration, and this response was amplified with increasing bacterial inoculum doses, indicating a significant interaction between As(III) exposure and inoculation. Chlorophyll a (Cla) and chlorophyll b (Clb) contents decreased in response to As(III), although the reduction became statistically significant only at concentrations above 5 µM. These changes were not modulated by bacterial inoculation. In contrast, carotenoid levels showed a clear dependence on both As(III) concentration and the inoculum dose of A. radioresistens MC-14 (As(III) × inoculation interaction). In non-inoculated plants, carotenoid levels increased in response to the highest As(III) concentration. However, in inoculated seedlings, carotenoid content was generally higher under control (As(III)-free) conditions compared to non-inoculated controls, and either remained stable (in the case of the lowest inoculum) or decreased with increasing As(III) concentrations, particularly at higher inoculum levels. Table 2 Gas exchange parameters: Anet (Net photosynthetic rate) and A dark (Dark respiration) (µmol CO 2 m −2 s − 1 ) and photosynthetic pigments concentration: Cla (Chlorophyll a ), Clb (Chlorophyll b ) and cars (Carotenoids and xhantophylls) (µg gDM − 1 ) in plants of A. thaliana growing at different As(III) concentrations as a function of A. radioresistens MC-14 inoculation after 30 days of culture. Data are average ± SE (n = 6). The last three rows are the F and p values of the analyses. As(III) (µM) Inoculum (OD600nm) A Adark Cla Clb Cars 0 0 4.8 ± 0.4 1.05 ± 0.10 16.1 ± 1.1 7.2 ± 0.5 2.0 ± 0.2 0.07 5.1 ± 0.4 1.11 ± 0.13 15.4 ± 0.8 8.1 ± 0.5 2.8 ± 0.2 0.21 4.5 ± 0.4 1.37 ± 0.40 16.3 ± 1.1 8.4 ± 0.9 2.4 ± 0.3 0.63 4.8 ± 0.45 1.20 ± 0.08 17.1 ± 0.8 8.1 ± 0.5 2.8 ± 0.1 3 0 4.3 ± 0.6 1.74 ± 0.32 16.6 ± 0.7 9.5 ± 2.2 1.7 ± 0.7 0.07 4.8 ± 0.4 1.26 ± 0.35 16.7 ± 1.0 7.4 ± 0.4 2.0 ± 0.2 0.21 4.9 ± 0.3 0.88 ± 0.13 17.1 ± 0.9 8.4 ± 0.7 2.0 ± 0.3 0.63 4.9 ± 0.6 1.08 ± 0.18 17.2 ± 0.8 8.0 ± 0.4 2.7 ± 0.2 5 0 5.6 ± 0.6 1.40 ± 0.37 16.4 ± 0.8 8.7 ± 1.5 1.9 ± 0.6 0.07 4.2 ± 0.7 1.31 ± 0.17 16.5 ± 0.6 7.6 ± 0.4 2.3 ± 0.2 0.21 4.8 ± 0.5 0.99 ± 0.11 17.2 ± 0.9 7.5 ± 0.4 2.1 ± 0.2 0.63 4.2 ± 0.4 1.11 ± 0.12 15.5 ± 1.6 7.3 ± 0.9 1.6 ± 0.4 10 0 12.2 ± 1.2 2.4 ± 0.56 12.5 ± 0.4 6.0 ± 1.0 2.5 ± 0.3 0.07 4.7 ± 1.4 1.93 ± 0.93 11.7 ± 1.1 4.7 ± 0.6 1.7 ± 0.2 0.21 5.4 ± 0.6 1.48 ± 0.25 13.7 ± 0.8 6.8 ± 0.5 2.2 ± 0.2 0.63 5.7 ± 0.4 2.96 ± 0.84 14.1 ± 0.5 6.5 ± 0.3 2.3 ± 0.1 As (III) F = 11.3; p = 0.0014 F = 18.87; p < 0.001 F = 7.8; p = 0.005 F = 3.63; p = 0.014 F = 3.6; p = 0.014 Inoculation F = 0.65; p = 0.58 F = 1.82; p = 0.18 F = 2.5; p = 0.47 F = 1.27; p = 0.29 F = 1.3; p = 0.29 As (III)×inoculation F = 3.10; p = 0.036 F = 4.44; p = 0.036 F = 1.5; p = 0.66 F = 0.63; p = 0.77 F = 3.38; p = 0.019 Maximum quantum efficiency of PSII photochemistry (Fv/Fm) remained consistently high (> 0.79) across all treatment conditions, indicating the absence of severe photoinhibition (Table 3 ). In contrast, the effective quantum yield of PSII (ΦPSII), photochemical quenching (qP), and non-photochemical quenching (qN) increased progressively with higher As(III) concentrations, particularly at the highest concentration tested. Inoculation with A. radioresistens MC-14 further enhanced these fluorescence parameters at low As(III) levels, suggesting a stimulatory effect on photosynthetic efficiency and energy dissipation under mild stress. However, at elevated As(III) concentrations, bacterial inoculation moderated the increases in ΦPSII, qP, and qN observed in non-inoculated plants, indicating a buffering effect on photochemical stress responses (As(III) × inoculation interaction). Table 3 Fluorescence parameters in plants of A. thaliana growing at different As(III) concentrations as a function of A. radioresistens inoculation after 30 days of culture. Data are average ± SE (n = 6). The last three rows are the F and p values of the analyses. As(III) (µM) Inoculum Fv/Fm ΦPSII qP qN (OD600nm) 0 0 0.80 ± 0.01 0.12 ± 0.01 0.16 ± 0.01 0.44 ± 0.03 0.07 0.79 ± 0.01 0.11 ± 0.01 0.16 ± 0.01 0.49 ± 0.01 0.21 0.79 ± 0.01 0.12 ± 0.01 0.17 ± 0.01 0.51 ± 0.02 0.63 0.80 ± 0.01 0.11 ± 0.01 0.15 ± 0.01 0.45 ± 0.02 3 0 0.79 ± 0.01 0.10 ± 0.01 0.15 ± 0.01 0.42 ± 0.02 0.07 0.76 ± 0.03 0.11 ± 0.01 0.18 ± 0.03 0.50 ± 0.03 0.21 0.80 ± 0.01 0.12 ± 0.01 0.17 ± 0.01 0.51 ± 0.02 0.63 0.78 ± 0.01 0.12 ± 0.01 0.18 ± 0.02 0.49 ± 0.04 5 0 0.79 ± 0.01 0.13 ± 0.01 0.19 ± 0.02 0.55 ± 0.02 0.07 0.78 ± 0.01 0.09 ± 0.01 0.13 ± 0.01 0.47 ± 0.02 0.21 0.79 ± 0.01 0.11 ± 0.01 0.16 ± 0.01 0.52 ± 0.02 0.63 0.79 ± 0.01 0.11 ± 0.01 0.15 ± 0.01 0.44 ± 0.03 10 0 0.80 ± 0.01 0.21 ± 0.01 0.3 ± 0.03 0.56 ± 0.06 0.07 0.80 ± 0.01 0.12 ± 0.01 0.16 ± 0.02 0.39 ± 0.03 0.21 0.79 ± 0.01 0.15 ± 0.01 0.21 ± 0.02 0.51 ± 0.02 0.63 0.79 ± 0.01 0.16 ± 0.01 0.24 ± 0.01 0.56 ± 0.02 As (III) F = 37.7; p = 0.92 F = 23.5; p < 0.001 F = 40.0; p = 0.007 F = 5.64; p = 0.018 Inoculation F = 41.5; p = 0.10 F = 1.78; p = 0.15 F = 45.3; p = 0.85 F = 0.84; p = 0.47 As (III)×inoculation F = 42.7; p = 0.85 F = 2.15; p = 0.095 F = 45.9; p = 0.26 F = 7.09; p < 0.001 Discusion Genome characterization, taxonomic confirmation, and mobile genetic elements in A. radioresistens MC-14 Whole-genome sequencing of A. radioresistens MC-14 confirmed its taxonomic identity, in agreement with previous classification based on 16S-ITS rDNA analysis (Molina et al., 2019 ). Average Nucleotide Identity (ANI) comparisons between A. radioresistens MC-14 and other A. radioresistens strains revealed values exceeding 99.5%, whereas comparisons with other Acinetobacter species yielded ANI values below 75%. These results are consistent with established phylogenetic boundaries within the genus (de Almeida et al., 2021 ), reinforcing the robust species-level classification of MC-14. The high degree of genomic similarity among A. radioresistens strains suggests for clonal dissemination, possibly facilitated by host transitions between humans and animals. This observation supports previous findings indicating interspecies transmission and genomic stability within this taxon (Roca et al., 2024 ). The comparative genomic analysis of A. radioresistens MC-14 with closely related strains provides valuable insights into both the conserved and strain-specific features of this species. The identification of 2,465 core genes shared among the four A. radioresistens strains analyzed suggests a relatively stable genomic backbone that likely encodes essential functions for environmental survival and stress resistance, characteristic of A. radioresistens . The presence of 202 unique singleton genes in A. radioresistens MC-14, as reported by Zhao et al. ( 2023 ), suggests that these genes are primarily associated with environmental niche-adaptation functions. In our case, this may reflect adaptive genomic features linked to its specific ecological niche in J. montana seeds. These strain-specific genes could encode traits relevant to rhizosphere colonization, metal resistance, plant-microbe interactions, or metabolic specialization, and thus warrant further functional annotation and experimental validation. The relatively high number of shared genes with FDAARGOS_731, a strain isolated from human skin (Roca et al., 2024 ), suggests a degree of functional overlap that may reflect conserved stress response mechanisms. In contrast, the exclusivity of certain genes in A. radioresistens MC-14 may underpin its symbiotic association with plant hosts and its survival in heavy metal-polluted environments. Altogether, the ortholog analysis highlights the genomic plasticity within A. radioresistens and underscores the potential of A. radioresistens MC-14 as a model for studying plant-associated bacteria with bioremediation capabilities. As mentioned, our findings highlight the ecological adaptability and high propagation potential of A. radioresistens MC-14 across diverse environments, including human skin, animal gastrointestinal tracts, contaminated soils, adult plants, and seeds. This broad ecological range is consistent with the known plasticity of the Acinetobacter genus, which is characterized by a high prevalence of mobile genetic elements (Walter et al., 2020 ; Zhao et al., 2023 ). Such mobile elements facilitate horizontal gene transfer (HGT), contributing to both the functional diversification and taxonomic complexity of the genus. This supports the model of an open pangenome in Acinetobacter , in which phenotypic traits can be independently acquired in response to specific environmental pressures (de Almeida et al., 2021 ). Importantly, some mobile elements, particularly plasmids, appear to possess adaptive value. The presence of chromosomal orthologs in A. radioresistens MC-14 corresponding to genes typically located on plasmids in closely related strains suggests past events of horizontal gene transfer from plasmids to the chromosome, potentially followed by selection under specific environmental conditions (Walter et al., 2020 ). One such adaptation is arsenic resistance, a trait commonly associated with multiple copies of the ars operon (Andres and Bertin, 2016 ). In A. radioresistens MC-14, two distinct ars clusters were identified. Cluster 1 includes arsHB1C1R1C2 , comprising: arsH , encoding an oxidative damage protector (Páez-Espino et al., 2020); arsB1 , an As(III) efflux pump (Rosen, 2002 ); arsC1 , a thioredoxin-dependent arsenate reductase (Mukhopadhyay et al., 2002 ); arsR1 , a transcriptional repressor (Busenlehner et al., 2003 ); and arsC2 , a glutathione-dependent reductase (Mukhopadhyay et al., 2002 ). Cluster 2 contains arsB2R2C3 , with arsB2 encoding a second As(III) efflux transporter, arsR2 another transcriptional regulator, and arsC3 a second glutathione-dependent reductase (Messens and Silver, 2006 ). Both clusters show high sequence identity with ars operons from other A. radioresistens strains, supporting the hypothesis of horizontal acquisition and chromosomal integration of these resistance determinants in A. radioresistens MC-14 (Walter et al., 2020 ). This genomic configuration likely contributes to the strain’s high tolerance to arsenic-rich environments. Genes coding for potential heavy metals and metalloids resistance. The genomic identification of numerous putative resistance genes to metals and metalloids in A. radioresistens MC-14 underscores its ecological significance and points to its promising utility in biotechnological applications aimed at remediating environments contaminated with toxic metals. Although the strain displays moderate resistance compared to bacteria harboring specialized metal-resistance operons, its capacity to tolerate at least six distinct heavy metals aligns with the harsh geochemical conditions of its origin, the Monica mine, characterized by elevated concentrations of arsenic and other heavy metals (García-Salgado et al., 2012 ). Importantly, the high resistance of A. radioresistens MC-14 strain to both trivalent and pentavalent forms of arsenic suggest it can be classified as a hyper-resistant strain. This is particularly relevant in the context of plant-microbe interactions, as As(V) and As III are readily absorbed by plant roots and disrupt essential metabolic pathways. As(III), which is approximately 100-fold more toxic than As(V), exerts its toxicity primarily by irreversibly binding to protein dithiols, thereby inactivating key enzymes (Williams and Silver, 1984 ). As(V), a phosphate analog, uncouples phosphorylation reactions, thereby impairing energy metabolism (Finnegan and Chen, 2012 ). The remarkable resistance profile of A. radioresistens MC-14 places it among a select group of arsenic hyper-resistant bacteria, comparable to strains such as Pseudomonas putida RS-5 [15 mM As(III), 500 mM As(V)] (Chang et al., 2008 ), Serratia marcescens [15 mM As(III), 500 mM As(V)] (Botes et al., 2007 ), and Corynebacterium glutamicum ATCC 13032 [10 mM As(III), 300 mM As(V)] (Ordóñez et al., 2005 ). These microbial models have served as the basis for various bioremediation strategies aimed at detoxifying arsenic-contaminated environments (González-Benítez et al., 2021 ). The unique resistance mechanisms of A. radioresistens MC-14 further reinforce its potential as a novel candidate for arsenic bioremediation technologies. Genomic features associated with an endophytic lifestyle in A. radioresistens MC-14. The genome of A. radioresistens MC-14 encodes multiple traits associated with plant colonization and adaptation to the rhizosphere environment. Notably, it includes genes responsible for the biosynthesis of type IV pili, a structure broadly conserved among plant-associated bacteria. These pili contribute to bacterial motility, specifically twitching motility, and chemotaxis toward plant-derived chemoattractants such as root exudates (Böhm et al., 2007 ). Beyond motility, type IV pili play an essential role in mediating adhesion to plant surfaces, a critical step in successful colonization (Mitter et al., 2013 ). In addition to motility-related genes, the genome harbors gene clusters potentially involved in the production of exopolysaccharides, which are important for surface adhesion and biofilm formation on root surfaces. These features likely facilitate effective root colonization and persistence in the dynamic rhizospheric environment, which is characterized by fluctuating osmolarity and the presence of reactive oxygen species (ROS) and phytotoxins (Böhm et al., 2007 ; Miller and Wood, 1996 ). The genome of A. radioresistens MC-14 contains genes encoding type I, II, III, and VI secretion systems (Table S5), which play a crucial role in plant-microbe interactions. These secretion systems are involved in mediating defense responses as well as facilitating microbial colonization (Bernal et al., 2018 ; Coulthurst, 2013 ; Entila et al., 2024 ; Sessitsch et al., 2012 ; Teulet et al., 2022 ; Tseng et al., 2009 ). Furthermore, the genome includes genes related to the biosynthesis and transport of the phytohormone indole-3-acetic acid (IAA), supporting prior evidence that A. radioresistens MC-14 produces auxin and may modulate host plant growth through hormonal signaling (Molina et al., 2019 ). Interestingly, while several canonical genes commonly found in plant growth-promoting bacteria (PGPB) are present, the genome lacks genes involved in nitrogen fixation, plant cell wall-degrading enzymes, and flagellum biosynthesis. The absence of these genes suggests that A. radioresistens MC-14 occupies a niche as a rhizosphere-dwelling bacterium rather than an obligate endophyte. Its genomic profile reflects an adaptation strategy in which symbiotic association is maintained without full intracellular colonization, supporting the hypothesis that some symbiotic bacteria undergo genome streamlining as they transition to specialized ecological roles (McCutcheon et al., 2024 ). Bacteria colonization of roots and plant morpho-physiology. In the current study, A. radioresistens demonstrated successful colonization of A. thaliana roots, with bacterial localization restricted exclusively to the apoplastic space. Microscopic analyses confirmed that the bacterium remained extracellular, occupying the intercellular spaces of root tissues without penetrating host cells. Although A. radioresistens MC-14 originates from J. montana seeds, its confinement to the apoplastic compartment of A. thaliana roots is consistent with the notion that bacteria colonize their native host more efficiently (Wippel et al., 2021 ), supporting a non-invasive, associative interaction characteristic of facultative symbiosis. This spatial arrangement allows A. radioresistens MC-14 to maintain close contact with host tissues—potentially influencing plant physiology—while avoiding the strong immune responses typically triggered by intracellular invasion. Inoculation with A. radioresistens MC-14 significantly enhanced plant survival in substrates heavily contaminated with [As(III)], and notably promoted progression into advanced phenological stages associated with reproduction. This improved performance under arsenic stress may be attributed to the strain, eventually through their ability to resist high arsenic concentrations. The enhanced tolerance facilitated by A. radioresistens MC-14 not only supports vegetative growth under toxic conditions but also enables resource allocation to reproductive structures, a critical determinant of plant fitness and ecological success. These findings underscore the potential role of this bacterium as a microbial ally for sustaining plant reproduction and resilience in arsenic-contaminated environments. Arsenic exposure has been widely reported to impair net photosynthetic rate (Anet) by compromising the structural and functional integrity of the photosynthetic apparatus, particularly photosystem II (Fv/Fm), at elevated As(III) concentrations (Arikan-Abdulveli, 2025 ; Tofan et al., 2025 ). However, in our experiments, Fv/Fm values remained consistently above 0.79 across all treatments, indicating that photoinhibition or structural damage to PSII did not occur under the experimental conditions. This observation is consistent with previous studies in other plant species that also exhibited limited or no impact on Fv/Fm under arsenic exposure (Vezza et al., 2022), suggesting a species-dependent tolerance threshold. Despite the stability of Fv/Fm, As(III) exposure led to a reduction in the total concentration of photosynthetic pigments, particularly chlorophylls. These pigments are known to be highly susceptible to oxidative stress (Agathokleous et al., 2020 ), a common consequence of arsenic toxicity. The observed decline in pigment content, in the absence of photoinhibition, suggests that As(III) primarily exerts its deleterious effects at the biochemical level rather than through direct disruption of PSII photochemistry. The reduction of photosynthetic pigments, primarily driven by oxidative stress, is a well-documented response in plants exposed to arsenic, including agriculturally important species such as Vigna mungo (Srivastava et al., 2017 ) and Oryza sativa (Rahman et al., 2007 ). In our study, the decline in pigment concentration likely reflects not only oxidative damage but also the impairment of other components of the photosynthetic machinery. This pigment degradation was accompanied by an increase in non-photochemical quenching (qN), a protective mechanism that enhances energy dissipation to prevent the formation of long-lived, redox-active compounds capable of inducing further oxidative damage (Heber, 2002 ; Miyake et al., 2002 ). Moreover, a significant increase in dark respiration rates was observed in response to elevated As(III) levels, likely reflecting heightened detoxification activity and enhanced cellular repair processes. These findings collectively indicate that arsenic-induced oxidative stress triggers a multifaceted physiological response in plants. Interestingly, despite the oxidative burden, plants demonstrated an ability to maintain or even enhance photosystem II efficiency (ΦPSII) under high arsenic concentrations. This effect was likely mediated through increased photochemical quenching (qP) and other regulatory adjustments. However, the improved photosynthetic efficiency was insufficient to counterbalance the reduction in biomass accumulation. The concurrent increase in As(III) concentration and decrease in plant mass suggest that photoassimilates are being redirected toward detoxification and cellular maintenance rather than growth, highlighting a trade-off between survival and development under stress conditions. Inoculation with A. radioresistens MC-14 significantly reduced dark respiration (A dark ) and non-photochemical quenching (qN), suggesting a lower metabolic cost associated with detoxification and cellular repair. This resource reallocation likely facilitates enhanced growth, particularly under stress conditions. The beneficial effects of this symbiosis were most pronounced at the highest As(III) concentration tested (10 µM As(III)), where physiological indicators showed marked improvement. The genome of the A. radioresistens MC-14 seed strain encodes antioxidant defense enzymes such as peroxidases, catalases, and superoxide dismutases, which may directly contribute to the observed attenuation of oxidative damage. In addition, inoculated plants displayed elevated concentrations of carotenoids (cars) even in the absence of arsenic, a pigment class known for its radical scavenging properties and its central role in mitigating reactive oxygen species (ROS) (Xu & Rothstein, 2018 ). Interestingly, non-inoculated plants only exhibited a significant increase in the xanthophyll cycle pigments (C + X) at the highest As(III) concentration, whereas inoculated plants maintained elevated levels across all treatments. This constitutive enhancement of antioxidant pigment content in inoculated plants suggests a priming effect, whereby association with A. radioresistens MC-14 induces early activation of ROS defense mechanisms. This preemptive physiological adaptation may underlie the enhanced tolerance observed under subsequent oxidative stress conditions. The beneficial effects of A. radioresistens MC-14 on plant performance in arsenic-contaminated environments were strongly dependent on the bacterial inoculum concentration. Under As(III) exposure, low inoculum levels (0.07 OD 600nm ) failed to confer significant benefits to plant survival, growth, or reproductive success. In contrast, an intermediate inoculum concentration (0.21 OD 600nm ) consistently maximized plant fitness, enhancing survival and progression to reproductive stages. However, a further increase in inoculum concentration (0.63 OD 600nm ) did not lead to additional improvements and, in some parameters, even reduced the positive effects observed at the optimal intermediate dose. These results suggest a trade-off in the symbiotic interaction, wherein both insufficient and excessive bacterial colonization can limit the overall benefit to the host plant. At low colonization densities, the metabolic cost of maintaining the symbiont is minimal but potentially inadequate to elicit a meaningful physiological response. Conversely, high bacterial loads may impose excessive metabolic demands on the host or trigger defense responses, ultimately diminishing the benefits of the association. Similar findings have been reported by Liu et al. ( 2024 ), who noted that excessive microbial colonization can negatively impact host plants due to resource competition and immune activation. Thus, identifying and applying an optimal inoculum concentration is critical to maximize the beneficial outcomes of plant–microbe symbioses while minimizing associated physiological costs. Conclusions The plant seed microbiome, despite its complexity and ecological relevance, has historically received limited attention in microbiome studies. Our findings underscore that seed-associated microbial communities are not passive passengers, but dynamic and selective systems shaped by host evolutionary pressures. The selective pressure exerted by plants on their seed microbiota has likely driven co-evolutionary processes, resulting in the persistence of beneficial microbial assemblages across generations. These microbial consortia provide adaptive advantages, particularly under environmental stress conditions. Remarkably, even a single seed-isolated bacterium, such as A. radioresistens MC-14, carries a repertoire of functional genes encoded within its circular chromosome and associated plasmids. These genetic elements confer adaptive traits that may function independently or synergistically with plant regulatory pathways, enhancing plant survival and stress tolerance. This highlights the role of the seed microbiome not only as a source of symbiotic resilience but also as a potential evolutionary driver in plant adaptation to challenging environments. The genome sequence of A. radioresistens MC-14 offers key insights into the adaptive strategies of a bacterium uniquely equipped to withstand high concentrations of [As(III)], modulate oxidative stress, and tolerate moderate levels of other heavy metals. This strain also displays vertical transmission potential via maternal inheritance, enhancing its ecological and evolutionary relevance. Genomic analysis reveals that horizontal gene transfer and the presence of mobile genetic elements have been fundamental in facilitating the adaptation of A. radioresistens MC-14 to diverse ecological niches and lifestyles. The absence of specific metabolic and structural genes typically present in free-living relatives further suggests a degree of genome reduction aligned with an endosymbiotic lifestyle, in which the bacterium relies on the host plant for certain cellular functions. Moreover, the broad ecological range observed among A. radioresistens strains, as contrasted with the A. radioresistens MC-14 seed-associated strain, points to a capacity for non-specific habitat colonization. This ecological versatility, together with the observed genomic plasticity and prevalence of mobile elements, supports the concept of an open pangenome within the Acinetobacter genus and helps to refine our understanding of its complex phylogenetic structure. From a physiological perspective, the seed-borne bacterium A. radioresistens MC-14 alleviates auxin deficiencies induced by arsenic stress in the root during the early root development, supports the preservation of root structural integrity, reduces oxidative stress, and potentially activates cellular recovery mechanisms. These combined effects promote increased biomass allocation to both roots and leaves, contributing to improved vegetative growth under adverse conditions. This physiological advantage extends to later developmental stages, enhancing key fitness traits and increasing the likelihood of reproductive success. In this context, the bacterium acts as a “germinal backpack”, endowing the seed with genetic elements advantageous for survival in stressful environments. Finally, genome mining revealed several gene clusters likely involved in plant-association relationships, paving the way for the utilization of this bacterium as a potential candidate for phytostimulation and biofertilization of plants exposed to metal-contaminated conditions, offering a promising tool for the development of sustainable agricultural strategies. Abbreviations Absorbance at 600 nm: OD 600nm . Arsenate: As(V). Arsenite: As(III). Aerobic arsenite oxidase gene: aioA Chlorophyll a: Cla. Chlorophyll b: Clb. Total carotenoids: cars Dark respiration rate: A dark . Green florescent protein: GFP Horizontal gene transfer (HGT). Maximum potential quantum efficiency of Photosystem II: Fv/Fm Minimum inhibitory concentration: MIC Murashige and Skoog broth: MS Net photosynthetic rate: Anet Non-photochemical quenching: qN Photochemical quenching: qP Photosynthetic photon flux density: PPD Quantum yield of photosystem II: ΦPSII Wild-type phenotype Columbia-0: Col-0 2-(N-morpholino) ethanesulfonic acid monohydrate: MES monohydrate Statements and Declarations Funding This work was supported by grants TED2021-132135B-I00, PID2022-142540B-I00, PID2021-127841OA-I00, PID2023-151327OB-I00 and TED2021-129229B-I00 from the Ministry of Science and Innovation of Spain. PB has participated as a research assistant with the contract PEJ-2023-AI/BIO-26882 the Comunidad de Madrid. CSP was recipients of Formación del Profesorado Universitario (FPU) fellowship from the Ministry of Universities of Spain. The EDGAR platform is financially supported by the BMBF grant FKZ 031A533 within the NBI network. We thank Dr. Jochen Blom (Justus-Liebig-University Giessen, Germany) for creating and allowing us to use the EDGAR project. Competing Interests 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. Author Contributions Natalia González-Benítez: Writing – original draft, Writing – review & editing, , Validation, Formal analysis, Data curation, Conceptualization. Mª Carmen Molina: Writing – review, Methodology, Formal analysis. Stephan Pollman: Writing – review & editing. Mercedes Uscola: Writing – review & editing, Methodology, Data curation. Visualization. Gonzalo Durante-Rodríguez: Writing – review, Methodology, Formal analysis. Jesús Vicente Carbajosa: Validation, Data curation, Cristina Serrano-Pelejero: Methodology, Formal analysis, Validation, Data curation. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9048400","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618107931,"identity":"ca84218b-2bd5-4013-8fdc-07c136ddf820","order_by":0,"name":"Natalia 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\u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 containing their chromosome and plasmids 1, 2 and 3, indicating their size in bp.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/bc57666e370e3703ae565733.png"},{"id":106626607,"identity":"36833905-a276-497d-855b-fa11e7b673aa","added_by":"auto","created_at":"2026-04-10 14:56:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":161991,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram representing the core genome and pan genome of the sequenced \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 strain, DD78, FDAARGOS_731 and LH6. The core and pan genome were calculated with the EDGAR program by iterative pairwise comparison of all genomes, taking as reference genome that of strain \u003cem\u003eA. radioresistens \u003c/em\u003eMC-14. The Venn diagram shows the number of reciprocal best hits between a subset of genomes. Therefore, the number of exclusive genes in each genome is always higher than the number of singletons (a gene without any hits against any other genome) for each strain.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/8f5a0aea471829fd8fa212da.png"},{"id":106626618,"identity":"d2c69b2d-31a7-4399-b093-ded2b551f93d","added_by":"auto","created_at":"2026-04-10 14:56:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":280956,"visible":true,"origin":"","legend":"\u003cp\u003eCell growth of \u003cem\u003eA. radioresistens \u003c/em\u003eMC-14 expressed as absorbance at 600 nm (OD\u003csub\u003e600nm\u003c/sub\u003e) at 24 h of incubation (end of exponential phase) in the presence of increasing concentrations of NiCl\u003csub\u003e2\u003c/sub\u003e (A), CrCl\u003csub\u003e2\u003c/sub\u003e (B), CoSO\u003csub\u003e4\u003c/sub\u003e (C), CuSO\u003csub\u003e4\u003c/sub\u003e (D), Pb(NO\u003csub\u003e3\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e (E), or ZnSO\u003csub\u003e4\u003c/sub\u003e (F). Error bars indicate the standard deviations of three independent experiments.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/23b95463161c3d6426a04f6c.png"},{"id":106626609,"identity":"75e625fe-e60c-483c-8d3a-9ce46c53a937","added_by":"auto","created_at":"2026-04-10 14:56:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56920,"visible":true,"origin":"","legend":"\u003cp\u003eCell growth of \u003cem\u003eA. radioresistens \u003c/em\u003eMC-14 expressed as absorbance at 600 nm (\u003cem\u003eOD\u003c/em\u003e\u003csub\u003e600nm\u003c/sub\u003e) at 24 h of incubation (end of exponential phase) in the presence of increasing concentrations of As(III). Error bars indicate the standard deviations of three independent experiments.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/f54473f28e495f354f9dcd0b.png"},{"id":106626588,"identity":"e8d32138-7ca0-454f-a8ae-5711cbdc7bb9","added_by":"auto","created_at":"2026-04-10 14:56:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":125981,"visible":true,"origin":"","legend":"\u003cp\u003eOrganization of the \u003cem\u003ears\u003c/em\u003e clusters in \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14, \u003cem\u003eA. radioresistens\u003c/em\u003e DSM6976 and \u003cem\u003eA. radioresistens \u003c/em\u003eFDAARGOS_731. Genes are represented by arrows as follows: \u003cem\u003earsH\u003c/em\u003e, oxidorreductase; \u003cem\u003earsB\u003c/em\u003e, As(III) transporter; \u003cem\u003earsCt\u003c/em\u003e, glutaredoxin-dependent arsenate reductase, \u003cem\u003earsR\u003c/em\u003e, transcriptional regulator; \u003cem\u003earsCg\u003c/em\u003e, thioredoxin-dependent arsenate reductase; \u003cem\u003earsO\u003c/em\u003e, monooxygenase. Location of each cluster in chromosome or plasmid is indicated. White arrows represent genes of unknown function.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/00da5b95ab9d3313c04531ad.png"},{"id":106626595,"identity":"c9c49262-7d63-48b8-9096-99ee0b9555d3","added_by":"auto","created_at":"2026-04-10 14:56:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":90089,"visible":true,"origin":"","legend":"\u003cp\u003eA cladogram tree is built from the multiple amino acid sequence alignment of the ArsR proteins using the program Phylogeny. The clustering of each ArsR from \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 is indicated in red and green areas.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/c9dc97bd2898aa52e9f1f94c.png"},{"id":106626591,"identity":"89f6d6e8-cb5b-4289-9d71-550db85de2af","added_by":"auto","created_at":"2026-04-10 14:56:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":369720,"visible":true,"origin":"","legend":"\u003cp\u003eRoots of \u003cem\u003eA. thaliana\u003c/em\u003eunder confocal laser microscopy after five days of germination and inoculation: (A) root appendage of control plant, and (B) root colonized by \u003cem\u003eA. radioresistens\u003c/em\u003e transformed with the pBAV1k-t5-gfp plasmid. Scale bar = 25µm. Arrows indicate fluorescence emitted by bacteria.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/4b4cdb504556a7f7fa869850.png"},{"id":106626629,"identity":"af00a0c5-6c4c-4946-9cce-b44915ad3709","added_by":"auto","created_at":"2026-04-10 14:56:45","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":134887,"visible":true,"origin":"","legend":"\u003cp\u003eProbability of each phenology phase of \u003cem\u003eA. thaliana\u003c/em\u003e plants growing at different As(III) concentrations as a function of the inoculum concentrations of \u003cem\u003eA. radioresistens\u003c/em\u003e (line type) after 30 days of culture. Note: 0 indicate no inflorescence perceptible; 1, inflorescence present but peduncle not elongated; 2, flower buds determined, and peduncle elongated; 3, first flower bud flourish, but most of them were not opened; 4, all flower buds already flourish, and some wilting of stamens and petals, which undergo abscission.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/45ea5c99129e71eb1d20d923.png"},{"id":106626631,"identity":"c18cee49-5786-49f6-9b36-e20afc2a7c26","added_by":"auto","created_at":"2026-04-10 14:56:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":86324,"visible":true,"origin":"","legend":"\u003cp\u003eMass (mean ± SE, n = 12) by fractions of \u003cem\u003eA. thaliana\u003c/em\u003e plants growing at different As(III) concentration as a function of the inoculum concentrations of \u003cem\u003eA. radioresistens\u003c/em\u003e after 30 days of culture\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/553e0ae2a1a0b7e9388904ad.png"},{"id":106626643,"identity":"27b34a9e-baaf-4f58-84c5-f633c0caf3a0","added_by":"auto","created_at":"2026-04-10 14:57:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3043478,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9048400/v1/507f6620-aa69-429f-b824-9c24c9502a35.pdf"}],"financialInterests":"","formattedTitle":"Seed Microbiota: A Key Factor in Plant Adaptation to Arsenic Stress ​","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eSeed‑borne endophytes represent a potential reservoir that supports plant survival under environmental stress.\u003c/li\u003e\n \u003cli\u003eSeed‑borne \u003cem\u003eA. radioresistens\u003c/em\u003e MC 14 apoplastically colonizes non‑native roots, forming a non‑invasive facultative symbiosis supported by key genomic traits that enable rhizosphere adaptation and plant association.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003eA. radioresistens\u003c/em\u003e MC 14 carries key genes that facilitate root colonization and rhizosphere adaptation, providing strong bioremediation potential.\u003c/li\u003e\n \u003cli\u003eUnder high arsenic stress, seed‑borne \u003cem\u003eA. radioresistens\u003c/em\u003e MC 14 improves plant fitness, stress tolerance, and successful progression into reproductive stages.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003ePollution, climate change, and biodiversity loss are among the most critical global environmental challenges, with pollution alone contributing to approximately nine million deaths annually\u0026mdash;one in six worldwide (Fuller et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Plant ecosystems, essential to both natural and agricultural productivity, are increasingly affected by these stressors. Abiotic factors such as arsenic contamination, alongside biotic pressures, directly impair plant development and biodiversity, posing a serious threat to global food security (Zandalinas et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Current projections for food production often neglect the compounded effects of climate change and arsenic contamination, which reduce crop yields and increase arsenic accumulation in seeds (Muehe et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This issue is particularly severe in rice, a staple for over 350\u0026nbsp;million people in developing regions, due to its high capacity for arsenic bioaccumulation (Geng et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Globally, arsenic contamination affects more than 140\u0026nbsp;million individuals across aquatic and terrestrial systems, frequently exceeding World Health Organization (WHO) safety thresholds (Babar \u0026amp; Tariq, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In response to such environmental pressures, plants have evolved various acclimation strategies to withstand adverse conditions. Over recent decades, evidence has highlighted the pivotal role of plant-microbe interactions, especially in the rhizosphere, in enhancing resilience to abiotic stress (Berendsen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). More recently, the endosphere has gained attention for its contribution to plant adaptability. In this context, seed endomicrobiota has a dual origin: horizontal transmission, mainly from soil and vertical transmission via maternal inheritance through seeds. Despite their biological importance, seed microbiomes\u0026mdash;particularly in wild plants\u0026mdash;have often been overlooked, despite exhibiting high taxonomic diversity throughout their life cycle (Chesneau et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Although the environmental and biological stressors that shape the seed microbiota remain insufficiently characterized, the genetic composition of seed microbiomes is increasingly recognized as a key factor in modulating plant responses to environmental fluctuations and influencing the evolutionary trajectory of the plant metaorganism (Abdullaeva et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gonz\u0026aacute;lez-Ben\u0026iacute;tez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Matsumoto et al., 2021; Nelson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wallace, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ecological selection has recently been identified as the principal mechanism influencing the succession of dominant taxa during the process of seed filling and maturation (Chesneau et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nelson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although plant-bacterial interactions are increasingly recognised for enhancing resilience under arsenic stress (Molina et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the role of maternally inherited seed microbiota in detoxification, adaptation, and evolution remains largely unexplored. Preliminary evidence suggests their importance; for example, \u003cem\u003eRhodococcus rhodochrous\u003c/em\u003e, a seed endophyte from \u003cem\u003eJasione montana\u003c/em\u003e, can modulate the phenotypic responses of arsenic-sensitive \u003cem\u003eJ. sessiliflora\u003c/em\u003e, conferring enhanced tolerance under arsenic exposure (Gonz\u0026aacute;lez-Ben\u0026iacute;tez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sequencing the genomes of such endophytes offers a novel approach to understanding their functional potential, often overlooked in seed physiology studies. This strategy enables the identification of beneficial genes, their interactions with the plant genome, and mechanisms of stress resilience. It also supports biofertilizer development and biotechnological innovations for bioremediation.\u003c/p\u003e \u003cp\u003eAmong seed-associated bacteria, \u003cem\u003eAcinetobacter\u003c/em\u003e species\u0026mdash;frequently found in ovules and seeds (Verma and White, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u0026mdash;have shown notable arsenic-transforming capabilities. Certain strains can oxidize over 80% of As(III) to As(V), linked to the presence of the \u003cem\u003eaioA\u003c/em\u003e gene (Basu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dutta et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eA. radioresistens\u003c/em\u003e, in particular, stands out for its phenotypic diversity, including plant growth promotion (Lafi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zapata-Sifuentes et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), pollutant degradation (Cattuci et al., 2025; Hidalgo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., 2020; Macaya et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Xiang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and extremotolerance (McCoy et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), largely attributed to its genomic plasticity and mobile genetic elements (Gentilini et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Walter et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The MC-14 strain of \u003cem\u003eA. radioresistens\u003c/em\u003e, isolated from \u003cem\u003eJ. montana\u003c/em\u003ein in an arsenic-contaminated area (Garc\u0026iacute;a-Salgado et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), is cultivable under standard conditions and exhibits arsenate resistance (MIC\u0026thinsp;=\u0026thinsp;200 mM), antifungal activity, and plant growth-promoting traits (Molina et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These features make it an ideal model for exploring how seed microbiomes contribute to plant fitness and adaptation under arsenic stress, and for investigating the functional potential of the plant hologenome.\u003c/p\u003e \u003cp\u003eIn this study, we propose the following objectives: (1) to perform a complete genome sequencing of the \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 strain, with a particular emphasis on identifying genes implicated in detoxification and metabolic pathways related to arsenic and heavy metal stress, the presence of mobile genetic elements involved in horizontal gene transfer, and plant interaction genes; and (2) to assess and quantify the beneficial effects of this seed-derived strain on plant fitness and adaptation using Arabidopsis thaliana as a model host species for inoculation experiments. Genomic characterization will shed light on the functional contribution of maternally inherited microbiota to plant physiological adaptation under arsenic stress. This knowledge could inform the application of such bacteria in phytostimulation, and detoxification strategies, ultimately contributing to more sustainable agricultural practices and improved food quality.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eBiological materials, culture media and growth conditions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eAcinetobacter radioresistens\u003c/em\u003e MC-14 was previously isolated from seeds of \u003cem\u003eJasione montana\u003c/em\u003e plants (Molina et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a highly arsenic tolerant plant species (Benson et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1981\u003c/span\u003e), collected from the vicinity of the Monica mine (Bustarviejo, Madrid) an area characterized by arsenic-contaminated soils with concentrations ranging from 0.3 to 30 g\u0026middot;kg⁻\u0026sup1; (Garc\u0026iacute;a-Salgado et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor whole-genome sequencing, \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 was cultivated aerobically in LB medium at 30\u0026deg;C. Upon obtaining sufficient mass, genomic DNA was extracted and sequenced by MicrobesNG company (Birmingham, U.K) following a combination of Nanopore long-read sequencing (Clarke et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Illumina short-read sequencing (250-nt paired-end reads) to refine the genome sequence.\u003c/p\u003e \u003cp\u003eTo assess resistance of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 to metals and metalloids, bacterial cultures were grown in sodium As(III) (NaAsO₂; 0\u0026ndash;40 mM; Merck), as well as nickel chloride (NiCl₂), chromium(II) chloride (CrCl₂), cobalt(II) sulfate (CoSO₄), copper(II) sulfate (CuSO₄), lead(II) nitrate [Pb(NO₃)₂], and zinc sulfate (ZnSO₄) at concentrations ranging from 0 to 5 mM (Sigma-Aldrich). After 24 hours of incubation at 30\u0026deg;C (end of exponential growth phase), bacterial growth was evaluated spectrophotometrically by measuring optical density at 600 nm (OD₆₀₀nm).\u003c/p\u003e \u003cp\u003eFor bacterial inoculation experiments, \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 was cultivated in LB broth until reaching OD\u003csub\u003e600nm\u003c/sub\u003e of 0.07, 0.21, and 0.63 at 600 nm, as measured with a Spectronic Genesys-8 spectrophotometer (Spectronic Instruments Inc.). After sterilization and cooling of 50 mL of MS broth medium, 0.1 mL of the bacterial suspension at the desired concentration was added to the growth plates.\u003c/p\u003e \u003cp\u003eAlthough the \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 strain originates from \u003cem\u003eJ. montana\u003c/em\u003e, the functional assays were conducted in \u003cem\u003eA. thaliana\u003c/em\u003e, the reference model in plant biology due to its fully sequenced genome, the availability of numerous mutant and transgenic lines, and standardized protocols for microscopic and genetic analyses (Ortega-Villaiz\u0026aacute;n et al., 2024). \u003cem\u003eArabidopsis thaliana\u003c/em\u003e seeds of the wild-type accession Columbia-0 (Col-0, stock N1092) were obtained from the Nothingham Arabidopsis Stock Centre (NASC).\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. thaliana\u003c/em\u003e was grown on sterile 0.5\u0026sdot; Murashige \u0026amp; Skoog (MS) medium plates (Murashige \u0026amp; Skoog, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1962\u003c/span\u003e) supplemented with 0.5 g of MES monohydrate L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of medium and 1% of agar (w/v). The pH was adjusted to 5.8 using KOH. To sterilize \u003cem\u003eA. thaliana\u003c/em\u003e seeds, they were first agitated in 70% ethanol for 2 minutes, followed by immersion in a sterilization solution containing 1% sodium dodecyl sulfate (w/v) and 2% sodium hypochlorite (w/v) in sterile distilled water for 12 min. The seeds were then washed five times with sterile MilliQ water. To confirm complete surface sterilization, the seeds were plated on Luria Bertani (LB) agar (Condalab). After 24 hours, the absence of bacterial growth confirmed sterilization.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMolecular and genomic, approaches to characterize\u003c/b\u003e \u003cb\u003eA. radioresistens\u003c/b\u003e \u003cb\u003eMC-14\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 was transformed with the pBAV1k-t5-gfp plasmid (Bryksin and Matsumura, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which conferred the ability to produce a green fluorescent protein (GFP) and harbors kanamycin resistance. GFP expressing \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 cells were grown at 28\u0026deg;C in LB medium supplemented with kanamycin at a concentration of 50 \u0026micro;gm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePlant Transformation and Co-Cultivation Conditions. \u003cem\u003eArabidopsis\u003c/em\u003e plants were transformed using \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e harboring the NPTII gene conferring resistance to kanamycin, following the floral dip method described by Clough and Bent (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The antibiotic kanamycin was included in the growth medium to prevent the loss of the GFP-encoding plasmid in \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 during plant-microbe co-cultivation assays.\u003c/p\u003e \u003cp\u003eGene prediction, genome annotation and sequence data analyses. The genome of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 was annotated using the Rapid Annotation using Subsystem Technology (RAST) pipeline (Overbeek et al., 2008). Functional annotation of predicted protein-coding genes was performed by comparison against several curated protein databases, including SwissProt, NCBI Protein, Clusters of Orthologous Groups (COG), Pfam, and SMART, using BLAST and RPS-BLAST algorithms (Altschul et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Pairwise and multiple sequence alignments of proteins were conducted using the ClustalW algorithm (Thompson et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) via the EMBL-EBI server. Phylogenetic analyses were performed using the Kimura two-parameter model (Kimura, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1980\u003c/span\u003e), and evolutionary trees were constructed with the neighbor-joining method (Saitou and Nei, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1987\u003c/span\u003e) implemented in the PHYLIP software package (Felsenstein, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Comparative genomic analyses were conducted using Geneious software, version 10.0.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.geneious.com/\u003c/span\u003e\u003cspan address=\"https://www.geneious.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Venn diagrams were generated with EDGAR (Blom et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The complete genome sequence and corresponding annotation of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 have been submitted to the NCBI GenBank database under accession number CP131483.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEffects of arsenic on\u003c/b\u003e \u003cb\u003eA. thaliana\u003c/b\u003e \u003cb\u003ecolonization by\u003c/b\u003e \u003cb\u003eA. radioresistens\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the impact of arsenic [As(III)] exposure and \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 inoculation on \u003cem\u003eA. thaliana\u003c/em\u003e phenotypic responses, a full-factorial experimental design was employed. The experiment comprised four arsenic concentrations (0, 3, 5, and 10 \u0026micro;M) and four bacterial inoculation levels, defined at OD₆₀₀ (0, 0.07, 0.21, or 0.63). Each treatment included three replicate plates, with six plants per plate (18 plants per treatment), resulting in a total of 24 plates (4\u0026times;2\u0026times;3), and a total of 144 plants.\u003c/p\u003e \u003cp\u003eSurface-sterilized \u003cem\u003eA. thaliana\u003c/em\u003e seeds were sown on 0.5x MS plates and stratified in the dark at 4\u0026deg;C for two days. Thereafter, plants were transferred to treatment media containing either 0 (control), 3, 5, or 10 \u0026micro;M As(III), along with varying concentrations of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 (OD₆₀₀ = 0, 0.07, 0.21, or 0.63), depending on the specific experimental condition.\u003c/p\u003e \u003cp\u003eFlorescence and gas exchange parameters were measured at the end of the cultivation period (30 days) in 4 plants per plate (12 plants per treatment). Measurements were conducted between 7:00 and 10:00 h (solar time) using an infrared gas analyzer (LICOR 6400XT, Li-Cor Inc., Lincoln NE, USA). Environmental conditions were adjusted to match those of the growth chamber; air temperature inside the cuvette was set to 20\u0026deg;C, CO\u003csub\u003e2\u003c/sub\u003e concentration to 400 \u0026micro;mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, vapor pressure deficit to 1.5, and photosynthetic photon flux density (PPD) to 800 \u0026micro;mol m\u003csup\u003e\u0026minus;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Initial measurements were performed under dark conditions (PPFD\u0026thinsp;=\u0026thinsp;0). Once the net photosynthetic rate (Aₙₑₜ) stabilized, the maximum potential quantum efficiency of Photosystem II (Fv/Fm) and dark respiration rate (A\u003csub\u003edark\u003c/sub\u003e) were recorded. Subsequently, light was applied, and after a minimum of 10 min, to allow the plants for light acclimation, one Anet stabilized again, the following parameters were measured: effective quantum yield of PSII (Φ\u003csub\u003ePSII\u003c/sub\u003e), photochemical quenching (qP), non-photochemical quenching (qN), and Anet.\u003c/p\u003e \u003cp\u003eThe phenological stage of each plant was also recorded based on the following scale: 0, no visible inflorescence; 1, inflorescence present but peduncle not elongated; 2, elongated peduncle with discernible flower buds; 3, first flower bud opened, but most of them remained closed; 4, all flower buds open, with onset of wilting and abscission of stamens and petals. Additionally, plant survival was recorded for all individuals. A plant was considered dead when all leaves had turned brown and no signs of new growth were observed for a period of at least five consecutive days.\u003c/p\u003e \u003cp\u003eThen, plants were harvested and divided into leaves, inflorescence and roots. Leaf samples were immediately frozen at \u0026minus;\u0026thinsp;30\u0026deg;C and stored until analysis of photosynthetic pigment concentrations. Pigment quantification was performed on the same plants used for gas exchange measurements.\u003c/p\u003e \u003cp\u003eLeaf subsamples (20 mg fresh weight) were extracted using pure dimethyl sulfoxide (DMSO) to determine the concentrations of chlorophyll \u003cem\u003ea\u003c/em\u003e (cla), chlorophyll \u003cem\u003eb\u003c/em\u003e (clb) and total carotenoids (xanthophylls\u0026thinsp;+\u0026thinsp;carotenoids; cars), following methodology described in Barnes et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Pigment concentrations were calculated using the equations provided by Wellburn (1994). All pigment values were expressed on a dry mass per area basis. Finally, all plant organs (leaves, inflorescences, and roots) were oven-dried and weighed to determine dry biomass.\u003c/p\u003e \u003cp\u003eBacterial colonization of plants. To visualize and localize \u003cem\u003eA. radioresistens\u003c/em\u003e within root tissues, at least five plants of transgenic \u003cem\u003eA. thaliana\u003c/em\u003e containing the NPII gene inoculated with \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 harboring the plasmid pBAV1k-t5-gfp for GFP expression. Plants were grown under controlled conditions in a growth chamber with a 16 h light / 8 h dark photoperiod at 22/18\u0026deg;C (light/dark), and a photon flux density of 400 \u0026micro;mol m⁻\u0026sup2; s⁻\u0026sup1;. Visualization was performed at the post-germination stage, five days after sowing. Visualization was conducted using a Zeiss LSM 880 confocal laser microscope with excitation using an Argon multiline laser at 488 nm and a 494/596 nm broadband filter for GFP detection. Differential interference contrast (DIC) imaging was applied, and observations were processed using the maximum intensity projection technique to enhance spatial resolution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis.\u003c/h2\u003e \u003cp\u003eAll analyses were performed using R version 4.3.0 (R Core Team, 2023). The fixed effects of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 inoculation (qualitative) and As(III) concentration (quantitative) were analyzed using linear mixed models including plate as a random factor. The influence of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 inoculation and As(III) concentration on plant phenological stage was evaluated using an ordinal multinomial analysis with the function \u003cem\u003epolr\u003c/em\u003e from the \u0026ldquo;\u003cem\u003eMASS\u003c/em\u003e\u0026rdquo; package (Venables and Ripley, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eWhole-genome analysis of\u003c/strong\u003e \u003cstrong\u003eA. radioresistens\u003c/strong\u003e \u003cstrong\u003eMC-14\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete genome of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 was sequenced using a hybrid approach combining Illumina and Oxford Nanopore technologies, as conducted by the MicrobesNG (Birmingham, UK) genome sequencing service. The Sequencing generated a total of 319,201 reads, resulting in approximately 30 \u0026times; genome coverage. The assembled genome consisted of a single circular chromosome with a predicted length of 3,161,054 base pairs, along with three plasmids measuring 21,734 bp (plasmid 1), 7,990 bp (plasmid 2), and 4,507 bp (plasmid 3), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Genome annotation predicted a total of 3,000 coding sequences and 98 RNA genes, including 77 tRNA and 21 rRNA genes.\u003c/p\u003e\n\u003cp\u003eWhole-genome comparison of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 with other \u003cem\u003eAcinetobacter radioresistens\u003c/em\u003e strains revealed high average nucleotide identity (ANI) values: 99.64% with strain FDAARGOS_731 (GCA_014069095; 63.5% genome alignment), 98.47% with DSM 6976 (GCA_000248115; 63.55% aligned), 98.58% with LH6 (CP030031; 62.99% aligned), 98.57% with DD78 (65.84% aligned), and 98.62% with DSSKY-A001 (74.29% aligned). In contrast, comparisons with other \u003cem\u003eAcinetobacter\u003c/em\u003e species such as \u003cem\u003eA. baylyi\u003c/em\u003e ADP1 and \u003cem\u003eA. variabilis\u003c/em\u003e RYU24 resulted in significantly lower ANI values of 73.95% (39.97% aligned) and 74.91% (38.94% aligned), respectively.\u003c/p\u003e\n\u003cp\u003eAnalysis of the 16S rRNA gene sequence confirmed the close phylogenetic relationship between \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 and other strains of the species, showing 100% identity with FDAARGOS_731, and \u0026gt;\u0026thinsp;99.7% identity with DSM 6976, DSSKY-A001, LH6, and DD18 (Figure S1). Notably, the GC content of the three plasmids identified in the MC-14 genome was markedly lower than that of the chromosome: 37.5% (plasmid 1), 34.1% (plasmid 2), and 31.6% (plasmid 3) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenome features of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 in comparison to those of strains FDAARGOS_731 and DSM 6976.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGenome features\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMC-14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eFDAARGOS_731\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eDSM 6976\u003csup\u003eT\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eOrigin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eA. thaliana\u003c/em\u003e seed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003ehuman skin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eoil soil\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTotal size (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3161054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3377444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3433938\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% chromosome (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e41.7 (3126823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e41.7 (3218180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e41.8 (3038795)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 1 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e37.5 (21734)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e37.5 (58767)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e37.1 (240231)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 2 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e34.1 (7990)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e37.1 (9483)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e39.2 (77605)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 3 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e31.6 (4597)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e34.9 (27601)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e42.7 (29910)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 4 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e39.4 (61168)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e36.2 (22527)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 5 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e39.1 (2245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e36.3 (9388)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 6 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e35.3 (5898)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 7 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e39.5 (5208)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGC% plasmid 8 (bp)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e--\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e32.7 (4376)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eProteins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e3302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e3330\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePlasmids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe \u003cem\u003eA. radioresistens\u003c/em\u003e strains included in the comparison originate from diverse habitats. Strain MC-14 was isolated from \u003cem\u003eJ. montana\u003c/em\u003e seeds collected from arsenic-contaminated soils; FDAARGOS_731 from human skin (Roca et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); LH6 from the avian intestinal tract (Crispen et al., 2018); DSM 6976 from cotton and gamma-sterilized soil (Nishimura et al., 1988); and DD78 from oil-contaminated sludge from the delta of the Aconcagua River. Ortholog analysis was performed to compare the genome of MC-14 with the closest strains. A Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e) illustrates the core and pan-genome composition of \u003cem\u003eA. radioresistens\u003c/em\u003e. A total of 2,465 genes were shared among the four analyzed strains. MC-14 shared 2,474 genes with DD18, 2,654 with FDAARGOS_731, and 2,472 with LH6. Additionally, 202 genes were identified as unique singletons exclusive to the MC-14 genome (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic insights into heavy metal and arsenic resistance in\u003c/strong\u003e \u003cstrong\u003eA. radioresistens\u003c/strong\u003e \u003cstrong\u003eMC-14.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe genomic analysis of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 revealed a substantial number of genes encoding proteins that may confer resistance to heavy metals and metalloids (Table S1).\u003c/p\u003e\n\u003cp\u003eMinimum inhibitory concentration (MIC) assays indicated tolerance levels of 2.5 mM for CoSO₄ and NiCl₂, \u0026gt;\u0026thinsp;5 mM for CrCl₂, 5 mM for Pb(NO₃)₂ and ZnSO₄, and 1 mM for CuSO₄ (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Remarkably, \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 strain demonstrated substantial resistance to As(III), with growth sustained up to 20 mM and observable even at 25 and 30 mM concentrations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eGenome annotation identified two distinct chromosomal gene clusters encoding arsenic resistance proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Cluster 1 consists of the \u003cem\u003earsHB1C1R1C2\u003c/em\u003e operon, while cluster 2 contains \u003cem\u003earsB2R2C3\u003c/em\u003e. Both clusters are located on the chromosome. Comparative analysis with the closely related strains DSM6976 and FDAARGOS_731 revealed conserved \u003cem\u003ears\u003c/em\u003e operon architecture; however, the genomic positioning of these clusters varies. In DSM6976, a gene cluster analogous that resembles cluster 2 of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 is chromosomally located, whereas a cluster resembling MC-14 strain\u0026rsquo;s cluster 1 is plasmid-encoded. A similar genomic arrangement was observed for FDAARGOS_731, with cluster 2-like genes chromosomally located and cluster 1-like genes present on a plasmid (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Interestingly, protein sequence comparisons revealed that the ArsR2 protein from MC-14 cluster 2 shares a higher degree of sequence identity with homologs encoded on plasmid-borne \u003cem\u003ears\u003c/em\u003e operons of DSM6976 and FDAARGOS_731 than with their chromosomal counterparts (Table S2). Phylogenetic analysis further supports this, showing that ArsR2 and other proteins encoded by \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 cluster 2 group closely with plasmid-derived \u003cem\u003ears\u003c/em\u003e proteins from these related strains (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Notably, plasmid 2 of DSM6976 and plasmid 1 of FDAARGOS_731 harbor not only genes related to resistance to heavy metals and metalloids but also numerous genes associated with horizontal gene transfer, suggesting that these \u003cem\u003ears\u003c/em\u003e operons may have been acquired via mobile genetic elements.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenomic determinants of an endophytic lifestyle\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAfter ten days of seedling growth, \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 established a robust biofilm on the root surface, which was easily observable (Figure S2). Detailed microscopic examination further demonstrated that the bacteria were predominantly located within the apoplastic compartment, occupying intercellular spaces without penetrating plant cells, thus confirming their extracellular position within the root tissue. Notably, no evidence of intracellular was observed, indicating that \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 adopts a non-invasive strategy, residing exclusively on the root surface and within intercellular spaces (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eWe conducted a comprehensive analysis of the \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 genome to identify genes potentially associated with traits relevant to plant-associated lifestyles. Genes related to bacterial motility were organized into distinct clusters encoding components of type IV pili and the chemotaxis apparatus (Table S3). The genome also harbors loci putatively involved in exopolysaccharide biosynthesis (Table S3), which are commonly implicated in biofilm formation and host interaction. Furthermore, we identified genes encoding enzymes such as catalases, superoxide dismutases, and peroxidases (Table S3), which are likely involved in reactive oxygen species (ROS) detoxification, a key feature for successful colonization of plant tissues. Genes orthologous to known osmolyte transporters, including those for choline and betaine uptake, were also present (Table S3), suggesting potential mechanisms for osmoregulation under plant-associated conditions. Notably, the genome encodes putative components of multiple secretion systems (types I, II, III, and VI) (Table S3). These systems are frequently associated with effector delivery, immune suppression, and promotion of intracellular colonization in plant hosts. The presence of gene clusters previously associated with plant-microbe interactions supports the hypothesis that \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 possesses genetic traits consistent with an endophytic lifestyle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlant growth promoting effects under As(III)-induced stress\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eA. thaliana\u003c/em\u003e exhibited full survival in control conditions lacking As(III), irrespective of bacterial inoculum concentration. In contrast, non-inoculated plants showed a marked decrease in survival at As(III) concentrations\u0026thinsp;\u0026ge;\u0026thinsp;5 \u0026micro;M, with only 36% of plants surviving exposure to 10 \u0026micro;M As(III). Notably, plant survival under As(III) stress was significantly influenced by the inoculation dose (interaction As(III) \u0026times; inoculation F\u0026thinsp;=\u0026thinsp;11.99; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Inoculation a low cell density (OD₆₀₀ = 0.07) did not confer any protective effect; however, higher inoculum densities (OD₆₀₀ = 0.21 or 0.63) resulted in complete plant survival even at the highest As(III) concentration tested (10 \u0026micro;M), highlighting a dose-dependent protective effect of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14.\u003c/p\u003e\n\u003cp\u003eThe phenological progression of \u003cem\u003eA. thaliana\u003c/em\u003e under As(III) exposure was significantly influenced by the bacterial inoculum dose (\u0026chi;\u0026sup2; = 165, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig. \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). In the absence of As(III), bacterial inoculation induced a slight delay in the development of the plants. Increasing As(III) concentrations markedly reduced the probability of plants reaching advanced flowering stages, with complete inhibition of reproductive development observed at the highest As(III) concentration. However, plants inoculated with \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 at OD₆₀₀ values of 0.21 or 0.63 demonstrated significantly improved progression to later phenological stages, even under the most severe As(III) stress. In absence of As(III) in the medium, inoculation did not result in significant differences in organ or plant mass. With increasing As(III) concentrations, a progressive reduction in both organ-specific and total biomass was observed. Importantly, these effects, particularly on inflorescence and root biomass, were significantly dependent on the inoculation dose (As(III) \u0026times; inoculation interaction, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.021 for all cases; Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e9\u003c/span\u003e). While low As(III) levels (e.g., 3 \u0026micro;M) elicited minimal growth promotion, increasing the bacterial inoculum enhanced plant growth at higher As(III) concentrations, with maximal biomass observed at OD₆₀₀ = 0.21.\u003c/p\u003e\n\u003cp\u003eNet photosynthetic rate (Aₙₑₜ) increased with rising As(III) concentrations, but this effect was restricted to non-inoculated seedlings (As(III) \u0026times; inoculation interaction, Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, dark respiration exhibited a positive correlation with As(III) concentration, and this response was amplified with increasing bacterial inoculum doses, indicating a significant interaction between As(III) exposure and inoculation. Chlorophyll a (Cla) and chlorophyll b (Clb) contents decreased in response to As(III), although the reduction became statistically significant only at concentrations above 5 \u0026micro;M. These changes were not modulated by bacterial inoculation. In contrast, carotenoid levels showed a clear dependence on both As(III) concentration and the inoculum dose of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 (As(III) \u0026times; inoculation interaction). In non-inoculated plants, carotenoid levels increased in response to the highest As(III) concentration. However, in inoculated seedlings, carotenoid content was generally higher under control (As(III)-free) conditions compared to non-inoculated controls, and either remained stable (in the case of the lowest inoculum) or decreased with increasing As(III) concentrations, particularly at higher inoculum levels.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGas exchange parameters: Anet (Net photosynthetic rate) and A\u003csub\u003edark\u003c/sub\u003e (Dark respiration) (\u0026micro;mol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e\u0026minus;2\u003c/sup\u003es\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and photosynthetic pigments concentration: Cla (Chlorophyll \u003cem\u003ea\u003c/em\u003e), Clb (Chlorophyll \u003cem\u003eb\u003c/em\u003e) and cars (Carotenoids and xhantophylls) (\u0026micro;g gDM\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in plants of \u003cem\u003eA. thaliana\u003c/em\u003e growing at different As(III) concentrations as a function of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 inoculation after 30 days of culture. Data are average\u0026thinsp;\u0026plusmn;\u0026thinsp;SE (n\u0026thinsp;=\u0026thinsp;6). The last three rows are the F and p values of the analyses.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAs(III) (\u0026micro;M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eInoculum \u003csub\u003e(OD600nm)\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eAdark\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eCla\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eClb\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eCars\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e9.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e17.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e16.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e17.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e15.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e12.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e11.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e4.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e1.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e13.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e14.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e6.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAs (III)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;11.3; p\u0026thinsp;=\u0026thinsp;0.0014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;18.87; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;7.8; p\u0026thinsp;=\u0026thinsp;0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.63; p\u0026thinsp;=\u0026thinsp;0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.6; p\u0026thinsp;=\u0026thinsp;0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eInoculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.65; p\u0026thinsp;=\u0026thinsp;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.82; p\u0026thinsp;=\u0026thinsp;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.5; p\u0026thinsp;=\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.27; p\u0026thinsp;=\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.3; p\u0026thinsp;=\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAs (III)\u0026times;inoculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.10; p\u0026thinsp;=\u0026thinsp;0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;4.44; p\u0026thinsp;=\u0026thinsp;0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.5; p\u0026thinsp;=\u0026thinsp;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.63; p\u0026thinsp;=\u0026thinsp;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;3.38; p\u0026thinsp;=\u0026thinsp;0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMaximum quantum efficiency of PSII photochemistry (Fv/Fm) remained consistently high (\u0026gt;\u0026thinsp;0.79) across all treatment conditions, indicating the absence of severe photoinhibition (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, the effective quantum yield of PSII (\u0026Phi;PSII), photochemical quenching (qP), and non-photochemical quenching (qN) increased progressively with higher As(III) concentrations, particularly at the highest concentration tested. Inoculation with \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 further enhanced these fluorescence parameters at low As(III) levels, suggesting a stimulatory effect on photosynthetic efficiency and energy dissipation under mild stress. However, at elevated As(III) concentrations, bacterial inoculation moderated the increases in \u0026Phi;PSII, qP, and qN observed in non-inoculated plants, indicating a buffering effect on photochemical stress responses (As(III) \u0026times; inoculation interaction).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\" class=\"fr-table-selection-hover\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eFluorescence parameters in plants of \u003cem\u003eA. thaliana\u003c/em\u003e growing at different As(III) concentrations as a function of \u003cem\u003eA. radioresistens\u003c/em\u003e inoculation after 30 days of culture. Data are average\u0026thinsp;\u0026plusmn;\u0026thinsp;SE (n\u0026thinsp;=\u0026thinsp;6). The last three rows are the F and p values of the analyses.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003eAs(III) (\u0026micro;M)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eInoculum\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eFv/Fm\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e\u0026Phi;PSII\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eqP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eqN\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e(OD600nm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAs (III)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;37.7; p\u0026thinsp;=\u0026thinsp;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;23.5; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;40.0; p\u0026thinsp;=\u0026thinsp;0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;5.64; p\u0026thinsp;=\u0026thinsp;0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eInoculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;41.5; p\u0026thinsp;=\u0026thinsp;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;1.78; p\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;45.3; p\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;0.84; p\u0026thinsp;=\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\n \u003cp\u003eAs (III)\u0026times;inoculation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;42.7; p\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;2.15; p\u0026thinsp;=\u0026thinsp;0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;45.9; p\u0026thinsp;=\u0026thinsp;0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c7\"\u003e\n \u003cp\u003eF\u0026thinsp;=\u0026thinsp;7.09; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discusion","content":"\u003cp\u003e \u003cb\u003eGenome characterization, taxonomic confirmation, and mobile genetic elements in\u003c/b\u003e \u003cb\u003eA. radioresistens\u003c/b\u003e \u003cb\u003eMC-14\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhole-genome sequencing of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 confirmed its taxonomic identity, in agreement with previous classification based on 16S-ITS rDNA analysis (Molina et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Average Nucleotide Identity (ANI) comparisons between \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 and other \u003cem\u003eA. radioresistens\u003c/em\u003e strains revealed values exceeding 99.5%, whereas comparisons with other \u003cem\u003eAcinetobacter\u003c/em\u003e species yielded ANI values below 75%. These results are consistent with established phylogenetic boundaries within the genus (de Almeida et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), reinforcing the robust species-level classification of MC-14. The high degree of genomic similarity among \u003cem\u003eA. radioresistens\u003c/em\u003e strains suggests for clonal dissemination, possibly facilitated by host transitions between humans and animals. This observation supports previous findings indicating interspecies transmission and genomic stability within this taxon (Roca et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe comparative genomic analysis of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 with closely related strains provides valuable insights into both the conserved and strain-specific features of this species. The identification of 2,465 core genes shared among the four \u003cem\u003eA. radioresistens\u003c/em\u003e strains analyzed suggests a relatively stable genomic backbone that likely encodes essential functions for environmental survival and stress resistance, characteristic of \u003cem\u003eA. radioresistens\u003c/em\u003e. The presence of 202 unique singleton genes in \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14, as reported by Zhao et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggests that these genes are primarily associated with environmental niche-adaptation functions. In our case, this may reflect adaptive genomic features linked to its specific ecological niche in \u003cem\u003eJ. montana\u003c/em\u003e seeds. These strain-specific genes could encode traits relevant to rhizosphere colonization, metal resistance, plant-microbe interactions, or metabolic specialization, and thus warrant further functional annotation and experimental validation. The relatively high number of shared genes with FDAARGOS_731, a strain isolated from human skin (Roca et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), suggests a degree of functional overlap that may reflect conserved stress response mechanisms. In contrast, the exclusivity of certain genes in \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 may underpin its symbiotic association with plant hosts and its survival in heavy metal-polluted environments. Altogether, the ortholog analysis highlights the genomic plasticity within \u003cem\u003eA. radioresistens\u003c/em\u003e and underscores the potential of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 as a model for studying plant-associated bacteria with bioremediation capabilities.\u003c/p\u003e \u003cp\u003eAs mentioned, our findings highlight the ecological adaptability and high propagation potential of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 across diverse environments, including human skin, animal gastrointestinal tracts, contaminated soils, adult plants, and seeds. This broad ecological range is consistent with the known plasticity of the \u003cem\u003eAcinetobacter\u003c/em\u003e genus, which is characterized by a high prevalence of mobile genetic elements (Walter et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such mobile elements facilitate horizontal gene transfer (HGT), contributing to both the functional diversification and taxonomic complexity of the genus. This supports the model of an open pangenome in \u003cem\u003eAcinetobacter\u003c/em\u003e, in which phenotypic traits can be independently acquired in response to specific environmental pressures (de Almeida et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Importantly, some mobile elements, particularly plasmids, appear to possess adaptive value. The presence of chromosomal orthologs in \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 corresponding to genes typically located on plasmids in closely related strains suggests past events of horizontal gene transfer from plasmids to the chromosome, potentially followed by selection under specific environmental conditions (Walter et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One such adaptation is arsenic resistance, a trait commonly associated with multiple copies of the \u003cem\u003ears\u003c/em\u003e operon (Andres and Bertin, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14, two distinct \u003cem\u003ears\u003c/em\u003e clusters were identified. Cluster 1 includes \u003cem\u003earsHB1C1R1C2\u003c/em\u003e, comprising: \u003cem\u003earsH\u003c/em\u003e, encoding an oxidative damage protector (P\u0026aacute;ez-Espino et al., 2020); \u003cem\u003earsB1\u003c/em\u003e, an As(III) efflux pump (Rosen, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); \u003cem\u003earsC1\u003c/em\u003e, a thioredoxin-dependent arsenate reductase (Mukhopadhyay et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); \u003cem\u003earsR1\u003c/em\u003e, a transcriptional repressor (Busenlehner et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e); and \u003cem\u003earsC2\u003c/em\u003e, a glutathione-dependent reductase (Mukhopadhyay et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Cluster 2 contains \u003cem\u003earsB2R2C3\u003c/em\u003e, with \u003cem\u003earsB2\u003c/em\u003e encoding a second As(III) efflux transporter, \u003cem\u003earsR2\u003c/em\u003e another transcriptional regulator, and \u003cem\u003earsC3\u003c/em\u003e a second glutathione-dependent reductase (Messens and Silver, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Both clusters show high sequence identity with \u003cem\u003ears\u003c/em\u003e operons from other \u003cem\u003eA. radioresistens\u003c/em\u003e strains, supporting the hypothesis of horizontal acquisition and chromosomal integration of these resistance determinants in \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 (Walter et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This genomic configuration likely contributes to the strain\u0026rsquo;s high tolerance to arsenic-rich environments.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenes coding for potential heavy metals and metalloids resistance.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe genomic identification of numerous putative resistance genes to metals and metalloids in \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 underscores its ecological significance and points to its promising utility in biotechnological applications aimed at remediating environments contaminated with toxic metals. Although the strain displays moderate resistance compared to bacteria harboring specialized metal-resistance operons, its capacity to tolerate at least six distinct heavy metals aligns with the harsh geochemical conditions of its origin, the Monica mine, characterized by elevated concentrations of arsenic and other heavy metals (Garc\u0026iacute;a-Salgado et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Importantly, the high resistance of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 strain to both trivalent and pentavalent forms of arsenic suggest it can be classified as a hyper-resistant strain. This is particularly relevant in the context of plant-microbe interactions, as As(V) and As III are readily absorbed by plant roots and disrupt essential metabolic pathways. As(III), which is approximately 100-fold more toxic than As(V), exerts its toxicity primarily by irreversibly binding to protein dithiols, thereby inactivating key enzymes (Williams and Silver, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). As(V), a phosphate analog, uncouples phosphorylation reactions, thereby impairing energy metabolism (Finnegan and Chen, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The remarkable resistance profile of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 places it among a select group of arsenic hyper-resistant bacteria, comparable to strains such as \u003cem\u003ePseudomonas putida\u003c/em\u003e RS-5 [15 mM As(III), 500 mM As(V)] (Chang et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), \u003cem\u003eSerratia marcescens\u003c/em\u003e [15 mM As(III), 500 mM As(V)] (Botes et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and \u003cem\u003eCorynebacterium glutamicum\u003c/em\u003e ATCC 13032 [10 mM As(III), 300 mM As(V)] (Ord\u0026oacute;\u0026ntilde;ez et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). These microbial models have served as the basis for various bioremediation strategies aimed at detoxifying arsenic-contaminated environments (Gonz\u0026aacute;lez-Ben\u0026iacute;tez et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The unique resistance mechanisms of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 further reinforce its potential as a novel candidate for arsenic bioremediation technologies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGenomic features associated with an endophytic lifestyle in\u003c/b\u003e \u003cb\u003eA. radioresistens\u003c/b\u003e \u003cb\u003eMC-14.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe genome of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 encodes multiple traits associated with plant colonization and adaptation to the rhizosphere environment. Notably, it includes genes responsible for the biosynthesis of type IV pili, a structure broadly conserved among plant-associated bacteria. These pili contribute to bacterial motility, specifically twitching motility, and chemotaxis toward plant-derived chemoattractants such as root exudates (B\u0026ouml;hm et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Beyond motility, type IV pili play an essential role in mediating adhesion to plant surfaces, a critical step in successful colonization (Mitter et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition to motility-related genes, the genome harbors gene clusters potentially involved in the production of exopolysaccharides, which are important for surface adhesion and biofilm formation on root surfaces. These features likely facilitate effective root colonization and persistence in the dynamic rhizospheric environment, which is characterized by fluctuating osmolarity and the presence of reactive oxygen species (ROS) and phytotoxins (B\u0026ouml;hm et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Miller and Wood, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). The genome of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 contains genes encoding type I, II, III, and VI secretion systems (Table S5), which play a crucial role in plant-microbe interactions. These secretion systems are involved in mediating defense responses as well as facilitating microbial colonization (Bernal et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Coulthurst, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Entila et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sessitsch et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Teulet et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tseng et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Furthermore, the genome includes genes related to the biosynthesis and transport of the phytohormone indole-3-acetic acid (IAA), supporting prior evidence that \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 produces auxin and may modulate host plant growth through hormonal signaling (Molina et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Interestingly, while several canonical genes commonly found in plant growth-promoting bacteria (PGPB) are present, the genome lacks genes involved in nitrogen fixation, plant cell wall-degrading enzymes, and flagellum biosynthesis. The absence of these genes suggests that \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 occupies a niche as a rhizosphere-dwelling bacterium rather than an obligate endophyte. Its genomic profile reflects an adaptation strategy in which symbiotic association is maintained without full intracellular colonization, supporting the hypothesis that some symbiotic bacteria undergo genome streamlining as they transition to specialized ecological roles (McCutcheon et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eBacteria colonization of roots and plant morpho-physiology.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the current study, \u003cem\u003eA. radioresistens\u003c/em\u003e demonstrated successful colonization of \u003cem\u003eA. thaliana\u003c/em\u003e roots, with bacterial localization restricted exclusively to the apoplastic space. Microscopic analyses confirmed that the bacterium remained extracellular, occupying the intercellular spaces of root tissues without penetrating host cells. Although \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 originates from \u003cem\u003eJ. montana\u003c/em\u003e seeds, its confinement to the apoplastic compartment of \u003cem\u003eA. thaliana\u003c/em\u003e roots is consistent with the notion that bacteria colonize their native host more efficiently (Wippel et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), supporting a non-invasive, associative interaction characteristic of facultative symbiosis. This spatial arrangement allows \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 to maintain close contact with host tissues\u0026mdash;potentially influencing plant physiology\u0026mdash;while avoiding the strong immune responses typically triggered by intracellular invasion.\u003c/p\u003e \u003cp\u003eInoculation with \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 significantly enhanced plant survival in substrates heavily contaminated with [As(III)], and notably promoted progression into advanced phenological stages associated with reproduction. This improved performance under arsenic stress may be attributed to the strain, eventually through their ability to resist high arsenic concentrations. The enhanced tolerance facilitated by \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 not only supports vegetative growth under toxic conditions but also enables resource allocation to reproductive structures, a critical determinant of plant fitness and ecological success. These findings underscore the potential role of this bacterium as a microbial ally for sustaining plant reproduction and resilience in arsenic-contaminated environments.\u003c/p\u003e \u003cp\u003eArsenic exposure has been widely reported to impair net photosynthetic rate (Anet) by compromising the structural and functional integrity of the photosynthetic apparatus, particularly photosystem II (Fv/Fm), at elevated As(III) concentrations (Arikan-Abdulveli, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tofan et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, in our experiments, Fv/Fm values remained consistently above 0.79 across all treatments, indicating that photoinhibition or structural damage to PSII did not occur under the experimental conditions. This observation is consistent with previous studies in other plant species that also exhibited limited or no impact on Fv/Fm under arsenic exposure (Vezza et al., 2022), suggesting a species-dependent tolerance threshold. Despite the stability of Fv/Fm, As(III) exposure led to a reduction in the total concentration of photosynthetic pigments, particularly chlorophylls. These pigments are known to be highly susceptible to oxidative stress (Agathokleous et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), a common consequence of arsenic toxicity. The observed decline in pigment content, in the absence of photoinhibition, suggests that As(III) primarily exerts its deleterious effects at the biochemical level rather than through direct disruption of PSII photochemistry. The reduction of photosynthetic pigments, primarily driven by oxidative stress, is a well-documented response in plants exposed to arsenic, including agriculturally important species such as \u003cem\u003eVigna mungo\u003c/em\u003e (Srivastava et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and \u003cem\u003eOryza sativa\u003c/em\u003e (Rahman et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In our study, the decline in pigment concentration likely reflects not only oxidative damage but also the impairment of other components of the photosynthetic machinery. This pigment degradation was accompanied by an increase in non-photochemical quenching (qN), a protective mechanism that enhances energy dissipation to prevent the formation of long-lived, redox-active compounds capable of inducing further oxidative damage (Heber, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Miyake et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Moreover, a significant increase in dark respiration rates was observed in response to elevated As(III) levels, likely reflecting heightened detoxification activity and enhanced cellular repair processes. These findings collectively indicate that arsenic-induced oxidative stress triggers a multifaceted physiological response in plants. Interestingly, despite the oxidative burden, plants demonstrated an ability to maintain or even enhance photosystem II efficiency (ΦPSII) under high arsenic concentrations. This effect was likely mediated through increased photochemical quenching (qP) and other regulatory adjustments. However, the improved photosynthetic efficiency was insufficient to counterbalance the reduction in biomass accumulation. The concurrent increase in As(III) concentration and decrease in plant mass suggest that photoassimilates are being redirected toward detoxification and cellular maintenance rather than growth, highlighting a trade-off between survival and development under stress conditions.\u003c/p\u003e \u003cp\u003eInoculation with \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 significantly reduced dark respiration (A\u003csub\u003edark\u003c/sub\u003e) and non-photochemical quenching (qN), suggesting a lower metabolic cost associated with detoxification and cellular repair. This resource reallocation likely facilitates enhanced growth, particularly under stress conditions. The beneficial effects of this symbiosis were most pronounced at the highest As(III) concentration tested (10 \u0026micro;M As(III)), where physiological indicators showed marked improvement. The genome of the \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 seed strain encodes antioxidant defense enzymes such as peroxidases, catalases, and superoxide dismutases, which may directly contribute to the observed attenuation of oxidative damage. In addition, inoculated plants displayed elevated concentrations of carotenoids (cars) even in the absence of arsenic, a pigment class known for its radical scavenging properties and its central role in mitigating reactive oxygen species (ROS) (Xu \u0026amp; Rothstein, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Interestingly, non-inoculated plants only exhibited a significant increase in the xanthophyll cycle pigments (C\u0026thinsp;+\u0026thinsp;X) at the highest As(III) concentration, whereas inoculated plants maintained elevated levels across all treatments. This constitutive enhancement of antioxidant pigment content in inoculated plants suggests a priming effect, whereby association with \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 induces early activation of ROS defense mechanisms. This preemptive physiological adaptation may underlie the enhanced tolerance observed under subsequent oxidative stress conditions.\u003c/p\u003e \u003cp\u003eThe beneficial effects of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 on plant performance in arsenic-contaminated environments were strongly dependent on the bacterial inoculum concentration. Under As(III) exposure, low inoculum levels (0.07 OD\u003csub\u003e600nm\u003c/sub\u003e) failed to confer significant benefits to plant survival, growth, or reproductive success. In contrast, an intermediate inoculum concentration (0.21 OD\u003csub\u003e600nm\u003c/sub\u003e) consistently maximized plant fitness, enhancing survival and progression to reproductive stages. However, a further increase in inoculum concentration (0.63 OD\u003csub\u003e600nm\u003c/sub\u003e) did not lead to additional improvements and, in some parameters, even reduced the positive effects observed at the optimal intermediate dose. These results suggest a trade-off in the symbiotic interaction, wherein both insufficient and excessive bacterial colonization can limit the overall benefit to the host plant. At low colonization densities, the metabolic cost of maintaining the symbiont is minimal but potentially inadequate to elicit a meaningful physiological response. Conversely, high bacterial loads may impose excessive metabolic demands on the host or trigger defense responses, ultimately diminishing the benefits of the association. Similar findings have been reported by Liu et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who noted that excessive microbial colonization can negatively impact host plants due to resource competition and immune activation. Thus, identifying and applying an optimal inoculum concentration is critical to maximize the beneficial outcomes of plant\u0026ndash;microbe symbioses while minimizing associated physiological costs.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe plant seed microbiome, despite its complexity and ecological relevance, has historically received limited attention in microbiome studies. Our findings underscore that seed-associated microbial communities are not passive passengers, but dynamic and selective systems shaped by host evolutionary pressures. The selective pressure exerted by plants on their seed microbiota has likely driven co-evolutionary processes, resulting in the persistence of beneficial microbial assemblages across generations. These microbial consortia provide adaptive advantages, particularly under environmental stress conditions.\u003c/p\u003e \u003cp\u003eRemarkably, even a single seed-isolated bacterium, such as \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14, carries a repertoire of functional genes encoded within its circular chromosome and associated plasmids. These genetic elements confer adaptive traits that may function independently or synergistically with plant regulatory pathways, enhancing plant survival and stress tolerance. This highlights the role of the seed microbiome not only as a source of symbiotic resilience but also as a potential evolutionary driver in plant adaptation to challenging environments.\u003c/p\u003e \u003cp\u003eThe genome sequence of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 offers key insights into the adaptive strategies of a bacterium uniquely equipped to withstand high concentrations of [As(III)], modulate oxidative stress, and tolerate moderate levels of other heavy metals. This strain also displays vertical transmission potential via maternal inheritance, enhancing its ecological and evolutionary relevance. Genomic analysis reveals that horizontal gene transfer and the presence of mobile genetic elements have been fundamental in facilitating the adaptation of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 to diverse ecological niches and lifestyles. The absence of specific metabolic and structural genes typically present in free-living relatives further suggests a degree of genome reduction aligned with an endosymbiotic lifestyle, in which the bacterium relies on the host plant for certain cellular functions. Moreover, the broad ecological range observed among \u003cem\u003eA. radioresistens\u003c/em\u003e strains, as contrasted with the \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 seed-associated strain, points to a capacity for non-specific habitat colonization. This ecological versatility, together with the observed genomic plasticity and prevalence of mobile elements, supports the concept of an open pangenome within the \u003cem\u003eAcinetobacter\u003c/em\u003e genus and helps to refine our understanding of its complex phylogenetic structure.\u003c/p\u003e \u003cp\u003eFrom a physiological perspective, the seed-borne bacterium \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 alleviates auxin deficiencies induced by arsenic stress in the root during the early root development, supports the preservation of root structural integrity, reduces oxidative stress, and potentially activates cellular recovery mechanisms. These combined effects promote increased biomass allocation to both roots and leaves, contributing to improved vegetative growth under adverse conditions. This physiological advantage extends to later developmental stages, enhancing key fitness traits and increasing the likelihood of reproductive success. In this context, the bacterium acts as a \u0026ldquo;germinal backpack\u0026rdquo;, endowing the seed with genetic elements advantageous for survival in stressful environments. Finally, genome mining revealed several gene clusters likely involved in plant-association relationships, paving the way for the utilization of this bacterium as a potential candidate for phytostimulation and biofertilization of plants exposed to metal-contaminated conditions, offering a promising tool for the development of sustainable agricultural strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAbsorbance at 600 nm: OD\u003csub\u003e600nm\u003c/sub\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eArsenate: As(V).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eArsenite: As(III).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAerobic arsenite oxidase gene: \u003cem\u003eaioA\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChlorophyll a: Cla.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChlorophyll b: Clb.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTotal carotenoids: cars\u003c/p\u003e\n\u003cp\u003eDark respiration rate: A\u003csub\u003edark\u003c/sub\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGreen florescent protein: GFP\u003c/p\u003e\n\u003cp\u003eHorizontal gene transfer (HGT).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMaximum potential quantum efficiency of Photosystem II: Fv/Fm\u003cbr\u003e\u0026nbsp;Minimum inhibitory concentration: MIC\u003cbr\u003e\u0026nbsp;Murashige and Skoog broth: MS\u003cbr\u003e\u0026nbsp;Net photosynthetic rate: Anet\u003cbr\u003e\u0026nbsp;Non-photochemical quenching: qN\u003cbr\u003e\u0026nbsp;Photochemical quenching: qP\u003cbr\u003e\u0026nbsp;Photosynthetic photon flux density: PPD\u003cbr\u003e\u0026nbsp;Quantum yield of photosystem II: ΦPSII\u003cbr\u003e\u0026nbsp;Wild-type phenotype Columbia-0: Col-0\u003cbr\u003e\u0026nbsp;2-(N-morpholino) ethanesulfonic acid monohydrate: MES monohydrate\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants TED2021-132135B-I00, PID2022-142540B-I00,\u0026nbsp;PID2021-127841OA-I00,\u0026nbsp;PID2023-151327OB-I00 and TED2021-129229B-I00\u0026nbsp;from the Ministry of Science and Innovation of Spain. PB has participated as a research assistant with the contract PEJ-2023-AI/BIO-26882 the Comunidad de Madrid. CSP was recipients of \u003cem\u003eFormación del Profesorado Universitario\u003c/em\u003e (FPU) fellowship from the Ministry of Universities of Spain. The EDGAR platform is financially supported by the BMBF grant FKZ 031A533 within the NBI network. We thank Dr. Jochen Blom (Justus-Liebig-University Giessen, Germany) for creating and allowing us to use the EDGAR project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNatalia González-Benítez: Writing – original draft, Writing – review \u0026amp; editing, , Validation, Formal analysis, Data curation, Conceptualization. Mª Carmen Molina: Writing – review, Methodology, Formal analysis. Stephan Pollman: Writing – review \u0026amp; editing. Mercedes Uscola: Writing – review \u0026amp; editing, Methodology, Data curation. Visualization. Gonzalo Durante-Rodríguez: Writing – review, Methodology, Formal analysis. Jesús Vicente Carbajosa: Validation, Data curation, Cristina Serrano-Pelejero: Methodology, Formal analysis, Validation, Data curation. Irene Cano: Methodology, Formal analysis,Validation, Weikang Huang: preliminary analysis, Manuel Carmona: Writing – original draft, Visualization, Validation, Methodology, Data curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe complete genome sequence and corresponding annotation of \u003cem\u003eA. radioresistens\u003c/em\u003e MC-14 have been submitted to the NCBI GenBank database under accession number CP131483. The rest of datasets generated during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdullaeva, Y., Ambika Manirajan, B., Honermeier, B., Schnell, S., \u0026amp; Cardinale, M. 2021. 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Doi:10.3390/plants11121612\u003c/li\u003e\n\u003cli\u003eZhao, Y., Wei, H. M., Yuan, J. L., Xu, L., \u0026amp; Sun, J. Q. 2023. A comprehensive genomic analysis provides insights on the high environmental adaptability of Acinetobacter strains. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1177951. Doi:10.1007/s40520-022-02325-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"seed endophyte, arsenite, plant growth promoting bacteria, metalloid resistance, Acinetobacter radioresistens MC 14","lastPublishedDoi":"10.21203/rs.3.rs-9048400/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9048400/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim:\u003c/strong\u003e Arsenic contamination represents one of the most critical anthropogenic stressors compromising organism resilience in the context of Global Change. However, some plant species can complete their life cycle in soils contaminated with this metalloid. Studies on plant–soil microbiome symbiosis have emphasized horizontal microbiome transmission (from soil to roots), while underestimating the role of vertically transmitted seed borne microbiomes. This work examines the seed borne endophytic bacterium \u003cem\u003eAcinetobacter radioresistens\u003c/em\u003e MC 14, isolated from arsenic hyper resistant plants \u003cem\u003eJasione montana\u003c/em\u003e and known for its arsenic tolerance and plant growth promoting traits. The study investigates its capacity to modulate plant phenotypic traits and enhance adaptation under arsenic stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e To this end, we evaluated the physiological responses of \u003cem\u003eArabidopsis thaliana\u003c/em\u003eexposed to As(III) concentrations following inoculation with \u003cem\u003eA. radioresistens \u003c/em\u003eMC 14, which apoplastically colonizes roots and establishes a non invasive facultative symbiosis that improves plant survival under arsenic stress.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e \u003cem\u003eA. radioresistens\u003c/em\u003e MC 14 improves plant fitness and ecological success, with optimal inoculum levels maximizing the benefits of the interaction while minimizing symbiotic costs. \u003cem\u003eA. radioresistens\u003c/em\u003e MC 14 mitigates arsenic induced phytohormonal imbalances in roots during early development. This bacterium–plant association promotes root growth and reduces As(III) triggered oxidative stress by activating cellular recovery mechanisms. As a result, plants produce more roots, flowers, and leaves even under toxic conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThese findings indicate that plants exert selective pressure on their seed microbiome, driving co evolution and maintaining beneficial microbial reservoirs across generations, ultimately enhancing plant performance in stressful environments.\u003c/p\u003e","manuscriptTitle":"Seed Microbiota: A Key Factor in Plant Adaptation to Arsenic Stress ​","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 14:55:11","doi":"10.21203/rs.3.rs-9048400/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2026-04-27T15:31:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-06T08:32:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2026-03-31T20:55:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T11:28:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2026-03-23T17:14:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c9d89276-2728-4328-8e2c-2d48df50aea7","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T14:55:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 14:55:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9048400","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9048400","identity":"rs-9048400","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}