Study of gamma radiation effects on the metabolic activity of Escherichia coli ATCC 25922 cellsAbstract
Investigating how Escherichia coli responds to gamma radiation enhances understanding of microbial functional mechanisms by elucidating how bacterial cells cope with ionizing radiation–induced DNA damage and oxidative stress. E. coli is a Gram-negative bacterium found in the intestinal microbiota of humans and widely used as a model organism in radiation microbiology. Exposure to ionizing radiation can impair cellular integrity, potentially leading to metabolic shifts and a loss of metabolic capacity. In this study, the effects of gamma radiation on E. coli populations were evaluated using MTT reduction assays as an operational proxy for metabolic activity rather than strict replicative viability. Irradiation experiments were performed using a fixed dose rate of 6 Gy/h with variable absorbed doses (0.05 to 40 Gy), as well as variable dose rates at a fixed dose of 6 Gy. Bacterial cells were suspended in BHI broth and incubated to allow biofilm formation. The results showed distinct metabolic profiles in planktonic and biofilm-associated cells at a constant dose rate, with dose-dependent alterations in metabolic output during biofilm formation.
Key words
ionizing radiation; E coli; metabolic activity study; bacterial behavior
INTRODUCTION
Beyond the various risks involving space missions, like cancer, chromosomal aberrations, risks to the central nervous system and to the cardiovascular system (Chancellor et al. 2014), microorganisms that are harmless on Earth can become pathogenic in space due to space conditions such as microgravity and radiation. A great part of this concern is due to the fact that in the space environment, alterations occur in the interaction between the microorganisms, medicines, environment and humans, increasing the risk of causing illness in astronauts (Leys et al. 2004). The first group of microorganisms examined in space were Escherichia coli, Staphylococcus aureus, and Enterobacter aerogenes (Kawaguchi et al. 2020). Bacteria, in general, are more sensitive to ionizing radiation, including gamma rays. In addition to causing damage to DNA, high doses of radiation can also induce damage to other cellular components (Confalonieri & Sommer 2011). However the increased resistance to radiation has been observed in different constituents of the Archaea and Bacteria domains (Rainey et al. 2005). As an example of this, we can highlight bacteria of the genus Deinococcus, certain strains of which are known to present high levels of resistance to ionizing radiation and, from what is available in terms of evidence, their radioresistance is due to the efficient repair of DNA damage (Battista 1997). However, other microorganisms can also present this radioresistance. Currently, strains of E. coli that are extremely resistant to ionizing radiation have been identified, exhibiting a new and complex phenotype obtained via directed evolution (Byrne et al. 2014). In addition to these genetic adaptations, the architectural organization of bacteria in biofilms, microbial communities that are embedded within extracellular polymeric matrix (Ciofu et al. 2022), significantly enhances their resilience to environmental stress, creating physical and chemical barriers that protect the community (Almatroudi 2025). Escherichia coli serves as a well-established model organism for studying stress responses, DNA damage repair, and adaptive mechanisms under radiation exposure, which are processes relevant to microbial survival in spacecraft environments and extraterrestrial habitats, making it suitable for investigating microbial responses to radiation exposure. Ionizing radiation represents one of the main environmental stressors in space, originating from galactic cosmic rays and solar particle events. Although gamma radiation is not the dominant radiation type in space, which varies from 3.33 to 3.76 mSv/year on the moon (Naito et al. 2023), equivalent to 0.00333 Gy and 0.00376 Gy, it is widely used in ground-based experiments as a controlled model to investigate microbial responses to ionizing radiation relevant to space environments (Jun et al. in press). The selected dose range (0.05 Gy to 40 Gy) specifically the lower doses (0.05 gy to 0.5 Gy) are similar with realistic cumulative exposures in spaceflight (0.3 mGy/day to 0.4 mGy/day) (Shavers et al. 2024). Since lethal doses for many bacteria occur in the hundreds to thousands of Gy, exposures in the range of approximately 1 Gy to 10 Gy fall within a sublethal regime and are therefore commonly employed to investigate stress-response thresholds, as they induce measurable DNA damage, oxidative stress, and toxicity in a dose-dependent manner without causing sterilization (Correa et al. 2019). Even at sublethal levels, ionizing radiation induces significant cellular responses in bacteria. In bacteria, doses as low as 0.25 Gy have been shown to activate the SOS regulon and prophage-associated pathways in a time-dependent manner (Sweet et al. 2023). Given that lethal doses typically occur in the kGy range, exposures between approximately 0.25 Gy and 10 Gy fall within a sublethal regime and are widely used to investigate stress-response thresholds, including DNA damage and oxidative stress responses (Sweet et al. 2023). Controlled irradiation experiments using defined dose rates allow the characterization of bacterial responses to ionizing radiation and provide a framework for understanding how microorganisms may respond to radiation stress in extreme environments, including space habitats, tough spaceflight microbiology experiments are still not very frequent when compared to the many thousands of terrestrial experiments that researchers perform annually worldwide (Nickerson et al. 2024). The gamma dose rates used in this study exceed those typically encountered in lunar environments; however, higher dose rates are commonly employed in laboratory experiments to accelerate radiation exposure and allow the observation of biological responses within practical experimental timeframes.
In order to comprehend what happens with E. coli cells when subjected to the effect of gamma rays, we used ATCC 25922 strains irradiated in suspension and in biofilm. The experiments were divided into two stages. In the first stage, different accumulated doses were used with the same dose rate and in the second stage, different dose rates were used with the same dose, in order to observe the effects of different parameters on the cell metabolic activity of the bacteria.
The analysis of the effects of gamma radiation on bacteria is of great relevance both for microbiology and for biotechnology and medicine, as radiation can alter the cellular behaviour of bacteria, impacting their survival, mutation, and resistance. Such studies are fundamental for the development of new sterilization methods and pathogen control. The molecular pathways governing the differential responses between biofilm and planktonic life to gamma radiation remain poorly understood. This underscores a critical need to elucidate how radiation-induced stress varies across different bacterial species and their specific life-history stages. Biofilms outperform planktonic life by providing protection against environmental stressors, resource management efficiency, and superior adaptive coordination in the face of environmental fluctuations (Baxter et al. 2026). The increased resilience of biofilms compared to planktonic cells is a broadly conserved phenomenon across diverse bacterial taxa, ranging from Gram-negative models like E. coli to complex phototrophic communities such as cyanobacteria (Baqué et al. 2013). Phenotypic studies evaluating bacterial responses under controlled irradiation conditions can contribute to characterizing these responses and provide a basis for future mechanistic investigations.
Despite studies and advances in this field, some resistance mechanisms, as well as the conditions that trigger these mechanisms, are not yet as exhaustive in current literature. Therefore, the present experimental work was developed to evaluate the metabolic evaluation post-irradiation of E. coli in suspension and in biofilm, under the effect of gamma radiation. This study does not assess reproductive viability or molecular mechanisms and is therefore restricted to detect metabolic activity after exposure to gamma radiation. With the data described, it is not possible to state specific molecular mechanisms (such as DNA repair, oxidative stress responses, among others). Due to limitations in available analytical instrumentation and the experimental timeframe, it was not possible to assess reproductive viability or investigate specific molecular mechanisms underlying the bacterial response to gamma radiation. Consequently, the analyses were restricted to global metabolic responses, and deeper mechanistic insights should be addressed in future studies using complementary methodologies and extended experimental designs. Below is a flowchart that illustrates the experimental methodology used.
MATERIALS AND METHODS
The study was divided into two experimental stages (Stage 1 and Stage 2). Stage 1 consisted of irradiation with Cobalt-60 under the condition of constant dose rate (6 Gy/h) and Stage 2 consisted of irradiation under constant dose (6 Gy). The experimental methodology for suspended cells and biofilms is illustrated below (Figure 1).
Experimental methodology. E. coli cells were seeded in a Petri dish for 24 h growth and transferred to a test tube for irradiation. After the irradiation, the bacteria were transferred to a 96-well plate for cell metabolic activity assay. Biofilm formation was noticed after 48 hours of incubation.
Strain description and sample preparation
The effects of gamma radiation on bacteria are well documented; however, the extent and nature of radiation-induced damage or potential adaptive responses remain incompletely understood. To characterize these effects, we analyzed 30 samples of E. coli exposed to gamma rays by quantifying bacterial metabolic activity using spectrophotometric assays. The results suggest that bacterial responses to radiation do not follow linear patterns. This non-linearity reflects the capacity of bacterial cells to activate stress-response (Jung et al. 2017) and DNA repair mechanisms prior to replication, rather than evolutionary processes occurring across multiple generations. Cells exposed to DNA-damaging agents activate conserved DNA damage response pathways whose primary function is to detect lesions and promote repair, thereby maintaining genomic integrity and minimizing the fixation of mutations. Because evolutionary change requires the accumulation, inheritance, and selection of mutations over successive replication cycles, such processes cannot be directly inferred from experiments that do not encompass multiple generations or assess heritable genetic alterations (Chatterjee & Walker 2017). Therefore, within the temporal and methodological scope of the present study, the observed outcomes should be interpreted as reflecting immediate physiological and repair responses to radiation-induced damage, rather than long-term evolutionary adaptation. Consequently, linear models are insufficient to adequately describe these dynamic cellular responses. In addition, variations in radiation dose, dose rate, exposure time, and radiation type can elicit non-linear and non-proportional biological effects, further limiting the applicability of linear assumptions in radiation–microorganism interaction studies.
The described methodology was developed in Costa (2023). Samples were prepared from lyophilized microorganisms by adding 1 ml of sterile saline solution to the bacteria vial. After activating the bacteria, 500 µl were used on lamb blood agar. For irradiation, three batches of E. coli colonies were collected from the agar and 25 ml of BHI broth were added to a falcon tube. From this procedure, more samples were prepared for the first stage of the experiment, with variable doses and constant dose rate. For the second stage of the experiment, the dose was kept constant and the dose rates were variable, in addition to the control group. Below is a flowchart indicating the irradiation methodology used.
Experimental Design
For each radiation treatment, four independent biological replicates were obtained from experiments conducted on four different days. Each biological replicate consisted of an independent bacterial culture. Within each biological replicate, technical replicates were performed to assess measurement variability. Specifically, the MTT assay was conducted in triplicate on the same sample, and the resulting values were averaged prior to statistical analysis. Technical replicates were not treated as independent observations. Therefore, the biological replicate was considered the experimental unit for all inferential analyses (n = 4 per treatment). All statistical tests, including non-parametric comparisons (Kruskal–Wallis followed by Dunn’s test), effect size estimation (Cliff’s delta with confidence intervals), and correlation analyses (Spearman’s rank correlation with bootstrap-derived confidence intervals), were performed using biological replicate-level data, with confidence intervals estimated via bootstrap procedures, and should be interpreted with caution given the limited sample size. The total number of raw measurements (including technical replicates) is reported for transparency but was not used to define the sample size in statistical analyses. Experimental design and sample size allocation are summarized in Tables I and II.
Illustrative flowchart of the irradiation protocol. Bacteria in suspension were exposed to gamma radiation under different conditions. After the biofilm formation the bacteria were also irradiated under different conditions.
The biological sample size (n) was defined as the number of independent bacterial cultures initiated from different colonies on different days, each processed through the complete experimental pipeline (growth, irradiation, and assay). The technical sample size was defined as repeated measurements obtained from the same biological sample, including multiple wells per plate and repeated instrument readings.
Cobalt-60 Irradiation
A Cobalt-60 isotopic source (ELDORADO E-78) from the Institute of Advanced Studies of the Department of Aerospace Science and Technology was used for irradiation. The samples were irradiated in suspension and biofilm according to the times and doses in the tables below. The biological response to ionizing radiation depends on the total accumulated dose, but it may also be influenced by the dose rate, that is, by the rate at which radiation is deposited in biological tissue. In other words, the effects may vary depending on whether the exposure occurs acutely or chronically. Table III presents the results obtained with the source-to-sample distance kept constant. Under these conditions, the dose rate remained fixed at 6 Gy/h, while the total accumulated dose ranged from 0.05 Gy to 40 Gy, according to the irradiation time of each sample.
This table summarizes the radiation treatments for constant dose rate and variable dose, sample sizes, and statistical use of the data. For each treatment, independent biological replicates (n) were obtained, and multiple technical replicates were performed per biological replicate to assess measurement variability. Technical replicates were averaged prior to statistical analysis and were not considered independent observations. Only biological replicates were used for inferential statistics, including non-parametric group comparisons, effect size estimation, and correlation analyses. Bootstrap resampling was applied where indicated to estimate confidence intervals.
This table summarizes the radiation treatments for variable dose rate and constant dose, sample sizes, and statistical use of the data. For each treatment, independent biological replicates (n) were obtained, and multiple technical replicates were performed per biological replicate to assess measurement variability. Technical replicates were averaged prior to statistical analysis and were not considered independent observations. Only biological replicates were used for inferential statistics, including non-parametric group comparisons, effect size estimation, and correlation analyses. Bootstrap resampling was applied where indicated to estimate confidence intervals.
In Table IV, the objective was to evaluate possible differences in the biological effects associated with different dose rates. To this end, the total accumulated dose was kept constant at 6 Gy, while the source-to-sample distance was varied, consequently changing the dose rate, along with the exposure time, so that all samples received the same final accumulated dose.
Stage 2 corresponds to the irradiation of biofilm and planktonic cells under variable dose rates.
The tests were performed with the samples behind 5 mm of acrylic to ensure the electronic equilibrium necessary to guarantee the correct dose, and all parameters of the test (time, distance, and dose rate) were kept under control during the tests. The time and distance measurements have negligible uncertainty (less than 1%), and the dose rate value is guaranteed by the facility within a combined uncertainty of ± 2%, as per dosimetric control performed with an ionization chamber calibrated against the national metrological reference. After irradiation, the bacteria were kept in a bacterial incubator for 12h (late exponential phase) at 37°C.
Biofilm Formation and Standardization
For biofilm formation, suspended samples were kept in an oven at 37°C for 24h (decline phase). The samples were standardized in a UV-Vis spectrophotometer at OD590.
The biofilm samples were seeded in 24-well plates, each occupying 11 wells, from the non-irradiated solution for the control group and from the irradiated solutions for a new irradiation with the same design as the control plate.
Preparation of the Microbial Cell Metabolic Activity and Sensibility Assay
For the preparation of the metabolic activity assay, we used MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide) to measure the respiratory activity of the cells. To achieve this, we distributed the prepared MTT solution into the 24-well plates containing the biofilm. The plates were incubated for 1 hour. For the sensitivity test, a DMSO (dimethyl sulfoxide) solution was used for quantification in the spectrophotometer. After the incubation period, the MTT solution was removed, and DMSO was added. The plates were then incubated again for 10 minutes and shaken on an orbital shaker for the same period. For the reading in the microplate reader, three aliquots were taken from each well of the 24-well plates. The reading was performed at 570 ηm
RESULTS
The experimental results are presented in two main phases: Stage 1, which focuses on irradiation of planktonic cells and biofilm at constant dose rate, and Stage 2, which addresses the irradiation of planktonic cells and biofilm under constant dose conditions.
From the analyses carried out in the work of Costa (2023), it was observed that linear models did not represent bacterial behaviour well, neither at constant rates nor at variable rates. This suggests that there is more than one cytoprotective mechanism used by bacteria, such as the DNA protection system (Puig et al. 2021) and biofilm formation (Bruhn et al. 2009).
Probability Distributions Fitting for Constant Dose Rate
After identifying a violation of the normality assumption in the dataset, evidenced by formal statistical tests it became necessary to adopt an alternative statistical approach, since many parametric methods rely on this assumption to produce valid inferences. Accordingly, different probability distributions were systematically evaluated in order to identify a model that more accurately represents the underlying behavior of the data, particularly given that, in biological contexts, values are typically positive, asymmetric, and driven by multiplicative processes. Among the distributions tested, the Weibull distribution provided the best fit for the Control Group data for constant dose rate, indicating that the logarithm of the observations approximates a normal distribution and that the observed asymmetry is consistent with this model. This choice was quantitatively supported by comparisons of the Akaike Information Criterion and the Bayesian Information Criterion, both of which penalize excessive model complexity and favor models that achieve the best balance between goodness of fit and parsimony. The agreement between AIC and BIC further reinforces the robustness of the Weibull distribution selection and demonstrates that the decision was based on objective and statistically sound criteria, thereby ensuring greater reliability of subsequent analyses.
For data from the 0.05 Gy, 0.1 Gy, 0.5 Gy, 1 Gy, 5 Gy, 10 Gy, and 40 Gy groups in suspension at a constant dose rate, the best distributions were Beta, Normal, Beta, Beta, Beta, Beta, and Normal, respectively. For data from groups 0.05 Gy, 0.5 Gy, 1 Gy, 5 Gy, 10 Gy, and 40 Gy in biofilm at constant dose rate, the distributions with the best fit were Normal, Weibull, Normal, Beta, Beta and Normal, respectively.
Goodness-of-fit analysis demonstrated that gamma radiation treatment not only decreased the metabolic activity of the cell but also shifted the distributive architecture of E. coli populations. While control and low-dose groups followed (Normal/Gamma) distributions, suggesting a baseline, homogeneous biological variability, higher doses were best modeled by the Beta distribution. All probability distribution fits are shown in the supplementary results (see Appendix S1)
This transition to a Beta regime is biologically relevant as it reflects a polarization of cellular response. The inherent flexibility of the Beta distribution in modeling clusters near the upper (resistance) and lower (mortality) boundaries suggests that radiation acts stochastically. This indicates that sub-populations within the biofilm (or suspension) may exhibit divergent survival outcomes, potentially driven by metabolic heterogeneity or physical shielding provided by the extracellular matrix.
Probability Distributions Fitting for Variable Dose Rate
Following the rejection of the normality hypothesis by goodness-of-fit tests, data modeling was redirected toward distributions capable of accommodating the non-linear nature and inherent heterogeneity of biological samples. The Beta distribution emerged as the most faithful representation for the control group at variable dose rate outperforming other candidates by accurately capturing the skewness and dispersion of the experimental values. The validation of this selection was established through the convergence of the AIC and BIC indices, where lower values confirmed that the Beta model provides maximum explanatory efficiency with minimal parameters. This approach ensures that derived inferences are not distorted by inadequate theoretical assumptions, grounding the study’s conclusions in a model that respects the actual frequency distribution observed.
For data from the 40.7 Gy/h, 20.1 Gy/h, 10 Gy/h, 6 Gy/h and 1 Gy/h groups in suspension at variable dose rate, the best distributions were Beta, Gamma, Normal, Beta, Beta. For biofilm groups the best distributions were Normal, Beta, Beta, Beta, Normal. All probability distribution fits are shown in the supplementary results (see Appendix S2)
The frequent selection of the Beta distribution across multiple dose rates and phenotypes highlights the inherent stochasticity of the bacterial response to ionizing radiation. Unlike the Normal distribution, which assumes a symmetric clustering around a mean, the Beta model’s prevalence suggests a polarization of survival outcomes. This shift indicates that, under varying dose rates, the population does not react as a uniform entity; instead, it exhibits a heterogeneous split where sub-populations either maintain high metabolic activity or are more susceptible to radiation-induced damage. Such distributional transitions provide mathematical evidence of the complex, non-linear survival dynamics that characterize E. coli in both planktonic and biofilm states.
Bacteria Irradiated at Constant Dose Rate in Suspension and Biofilm
Here are presented 3 tables regarding the descriptive statistics and the results of Kruskal - Wallis test for both suspension and biofilm groups. Table V presents the descriptive statistics of the values obtained for the experimental groups maintained in suspension and exposed to different doses of ionizing radiation (0.05 to 40 Gy), in comparison with the non-irradiated control group. The mean, median, standard deviation (SD), and interquartile range (IQR) are reported, allowing a robust assessment of central tendency and data dispersion. All irradiated groups exhibited higher mean values than the control, with particular emphasis on the higher doses (10 and 40 Gy), which showed the highest medians and the widest ranges of variation. The presence of discrepancies between mean and median values, together with relatively wide IQRs at certain doses, suggests a non-normal distribution and intragroup heterogeneity, thereby justifying the use of nonparametric statistical analyses. The coefficient of variation ranged from 28.35% to 43.50%, reflecting moderate variability among experimental groups.
Descriptive statistics of the analyzed variable for experimental groups maintained in suspension and exposed to increasing doses of ionizing radiation (0.05–40 Gy), in comparison with the non-irradiated control group. Data are summarized as mean, median, standard deviation (SD), and interquartile range (IQR). The table highlights dose-dependent changes in central tendency and variability, as well as deviations between mean and median values, indicating non-normal data distribution and intragroup heterogeneity.
Table VI compiles the descriptive statistics corresponding to the biofilm groups, also organized by radiation dose. As observed for the suspension groups, the data are presented in terms of mean, median, SD, and IQR, enabling direct comparisons across experimental conditions. A non-linear response to radiation is evident, with more pronounced increases at intermediate and higher doses, particularly at 1, 10, and 40 Gy. The observed variability, reflected in the standard deviation and interquartile range values, reinforces the presence of heterogeneous responses within groups, possibly associated with the greater structural and physiological complexity of the biofilm state. The coefficient of variation ranged from 28.65% to 43.50%, indicating a moderate level of dispersion in the data.
Descriptive statistics of the analyzed variable for experimental groups grown in biofilm and subjected to different doses of ionizing radiation (0.05–40 Gy). Results are expressed as mean, median, standard deviation (SD), and interquartile range (IQR). The data reveal a non-linear response to radiation exposure and increased variability at specific doses, reflecting heterogeneous responses likely associated with the structural and physiological complexity of the biofilm state.
Finally, Table VII presents the results of the Kruskal–Wallis test, applied separately to the suspension and biofilm groups to assess overall differences among radiation doses. For both experimental conditions, the high χ² values and extremely low p-values (p < 0.001) indicate the existence of statistically significant differences among groups, confirming that radiation dose exerted a measurable effect on the analyzed variable. These results corroborate the descriptive evidence presented in Tables V and VI and support the continuation of post hoc analyses to specifically identify the doses responsible for the observed differences.
Results of the Kruskal–Wallis nonparametric test applied to suspension and biofilm experimental groups to assess overall differences among radiation doses. The table reports the chi-square statistic (χ²), degrees of freedom (DF), and corresponding p-values. Statistically significant results indicate that ionizing radiation dose had a measurable effect on the analyzed variable, supporting the presence of dose-dependent differences across experimental conditions.
For both suspension and biofilm conditions, the control group exhibited the lowest mean and median metabolic activity values (mean ≈ 0.466), as shown in Tables V and VI, representing a baseline of reduced bacterial metabolic activity with moderate intragroup variability. In contrast, irradiated groups generally showed higher mean metabolic activity values, indicating a positive shift relative to the control.
Low doses (≤ 0.1 Gy), especially 0.05 Gy, yielded intermediate to elevated metabolic activity values in both experimental conditions, indicating that even minimal radiation exposure may influence bacterial responses. Increased variability, reflected by higher standard deviation and interquartile range values at extreme doses (0.05 and 40 Gy), suggests heterogeneous adaptive responses among bacterial subpopulations.
Importantly, the Kruskal–Wallis test revealed highly significant overall differences among radiation doses for both suspension (χ² = 48.66, p = 2.65 × 10⁻⁸) and biofilm conditions (χ² = 45.51, p = 3.71 × 10⁻⁸), confirming that radiation dose exerted a statistically significant global effect on bacterial metabolic activity.
Aligned Rank Transform ANOVA revealed a significant effect of dose and condition on the measured response (F(14, 480) = 8.34, p < 0.0001), indicating that the overall response differs among the combinations of radiation dose and bacterial growth condition.
Pairwise comparisons showed that several doses significantly differed from the control (0 Gy) within each condition. For example, in biofilm, exposures at 1 Gy, 10 Gy, and 40 Gy led to a significant increase/decrease in response compared to 0 Gy (p < 0.0001). In suspension, doses of 0.05 Gy, 1 Gy, 5 Gy, 10 Gy, and 40 Gy showed significant differences compared to 0 Gy (p < 0.05). Conversely, some low doses in biofilm, such as 0.05 Gy, did not significantly differ from the 0 Gy control (p = 1.000).
To evaluate potential alternative explanations for the observed responses specifically, Rival Hypothesis A (effect driven only by handling/time rather than radiation) and Rival Hypothesis B (effect due to assay artifacts or biofilm biomass) we first conducted an ART ANOVA. The analysis revealed a significant effect of the group factor on the response variable (F₁₄,₄₈₀ = 8.3411, p < 0.0001), indicating that at least one condition-dose combination differs from the others. This result justifies subsequent pairwise comparisons to determine which specific groups drive these differences.
Pairwise contrasts following the ART ANOVA revealed specific significant differences between groups. Comparisons such as biofilm at 0 Gy and suspension at 0.05 Gy, biofilm at 0 Gy and biofilm at 1 Gy, and biofilm at 0 Gy and suspension at 1 Gy indicated statistically significant changes in the response variable (p < 0.01 for some contrasts), suggesting that even small increases in dose or differences between biofilm and suspension conditions can impact the measured outcome. Other contrasts involving higher doses, such as biofilm at 0 Gy and biofilm at 40 Gy and biofilm at 0 Gy and suspension at 40 Gy, also showed significant differences, confirming that the effect of radiation increases with dose. These results highlight specific group differences without implying that all significant contrasts correspond to high-dose conditions.
Suspension Groups
In suspension, doses of 0.05 Gy (mean = 0.693), 1 Gy (mean = 0.648), 5 Gy (mean = 0.645), 10 Gy (mean = 0.681), and 40 Gy (mean = 0.736) displayed consistently higher mean and median values compared to the control group. Similarly, in biofilm-forming bacteria, increased mean metabolic activity was observed particularly at 1 Gy (mean = 0.706), 10 Gy (mean = 0.680), and 40 Gy (mean = 0.736). These patterns suggest a non-linear response to radiation exposure, with more pronounced increases at intermediate and higher doses.
The Kruskal-Wallis rank sum test, as shown in Table VII, revealed a eight significant pairwise differences in the distribution of values across the eight groups in suspension (p = 2.651× 10⁻⁸), indicating that not all groups share the same median ranks. Subsequent pairwise comparisons using Dunn’s test with Holm adjustment identified significant differences particularly involving control group, which differed from groups 0.05, 1, 5, 10, and 40 Gy. Additionally, groups 0.05 and 0.5, 0.5 and 10, and 0.5 and 40 also showed statistically significant differences after correction, highlighting control group and group 0.5 as key contributors to the overall variation.
Applying the Benjamini-Hochberg (FDR) adjustment further expanded the number of significant contrasts, reinforcing previous findings and revealing additional significant pairwise differences. The FDR approach was applied to complement the more conservative Holm correction, allowing the detection of broader response patterns while maintaining control over the expected proportion of false positives. Notably, group 0.1 Gy differed significantly from control group and 0.05, 10, and 40 Gy under this correction. Group 0.5 continued to show strong differences from groups 0.05, 5, 10, and 40 Gy. These results suggest that group 1 consistently exhibits lower or higher values compared to most other groups, and group 0.5 Gy plays a central role in group differentiation as well. Overall, the analysis highlights marked heterogeneity among the groups, with distinct patterns likely driven by specific treatment or condition effects.
A moderate positive trend was observed between gamma radiation dose and bacterial metabolic activity in suspension, as indicated by Spearman’s rank correlation (ρ = 0.60, p = 0.12). Bootstrap confidence interval analysis based on 10,000 replicates showed a wide 95% CI (–0.32 to 1.00), reflecting substantial uncertainty around the estimate and suggesting that the observed trend should be interpreted cautiously.
Cliff’s Delta analysis revealed predominantly large positive effect sizes across the evaluated radiation doses, indicating a consistent increase in the measured response relative to the control condition. Large effects were observed at 0.05 Gy (δ = 0.586; 95% CI: 0.303–0.774), 0.1 Gy (δ = 0.536; 95% CI: 0.311–0.704), 1 Gy (δ = 0.587; 95% CI: 0.353–0.752), 5 Gy (δ = 0.567; 95% CI: 0.305–0.749), 10 Gy (δ = 0.658; 95% CI: 0.401–0.820), and 40 Gy (δ = 0.589; 95% CI: 0.292–0.782). In all these cases, the confidence intervals did not include zero, supporting the presence of robust and biologically meaningful effects.
In contrast, the intermediate dose of 0.5 Gy exhibited a small positive effect size (δ = 0.319; 95% CI: 0.025–0.562), although its confidence interval was narrower and closer to zero, suggesting a comparatively weaker response at this dose. Overall, these results indicate a clear and sustained positive effect across a wide range of radiation doses, with particularly pronounced effects at higher doses, reinforcing the presence of a strong dose-dependent pattern in the response variable.
Biofilm Groups
The Kruskal-Wallis test indicated a highly significant overall effect in values across the seven treatment groups in biofilm (p= 3.714× 10⁻⁸), suggesting that at least one group distribution differs significantly from the others. Post hoc Dunn’s tests were performed to identify which specific groups differed. Using the Holm correction, statistically significant differences were observed between control group and groups 1, 10, and 40 Gy; between group 1 and group 10; between group 5 and groups 10 and 40 Gy; and between group 0.5 and group 40 Gy. These results emphasize that the control group showed the strongest divergence, particularly when compared to groups with notably higher or lower value distributions.
When applying the more permissive Benjamini-Hochberg (FDR) adjustment, additional significant differences emerged. In this analysis, comparisons involving group 0.05 (versus groups 1, 10, and 40), group 0.5 (versus groups 1, 10, and 40 Gy), and group 5 (versus groups 1, 10, and 40 Gy) also reached significance, reinforcing the previous findings while highlighting a broader pattern of divergence. Control group and groups 1, 5, and 40 Gy appeared the most frequently in significant comparisons, indicating that these groups contributed substantially to the overall differences detected by the Kruskal-Wallis test. These results suggest distinct group-specific effects or treatments that strongly influenced the measured values.
A positive trend between gamma radiation dose and biofilm formation was observed, as indicated by Spearman’s rank correlation (ρ = 0.68, p = 0.094). However, the 95% bootstrap confidence interval (–0.21 to 1.00, 10,000 replicates) indicates substantial uncertainty, suggesting that this trend should be interpreted cautiously.
Cliff’s Delta analysis was used to quantify the effect size of the different radiation dose groups on the measured bacterial response. The results indicated varying magnitudes of effect across treatments. For the lower doses, 0.05 Gy and 0.5 Gy, the effect sizes were small, with delta estimates of 0.316 (95% CI: 0.038–0.548) and 0.183 (95% CI: –0.082–0.424), respectively. In contrast, higher doses, including 1 Gy, 10 Gy, and 40 Gy, showed large effect sizes, with delta estimates of 0.521 (95% CI: 0.221–0.730), 0.658 (95% CI: 0.401–0.820), and 0.589 (95% CI: 0.292–0.782), respectively.
These results suggest a dose-dependent increase in the magnitude of effect, with higher doses producing more pronounced differences in the bacterial response. Notably, some confidence intervals at low doses included values near zero, indicating limited or variable effects at these lower radiation exposures, whereas the large effect sizes observed at higher doses were supported by confidence intervals that did not include zero, confirming consistent and substantial impacts.
Variable Dose Rate Irradiated Bacteria in Suspension and Biofilm
Table VIII presents the descriptive statistics of bacterial groups irradiated in suspension and exposed to different ionizing radiation dose rates. Mean, median, standard deviation, and interquartile range (IQR) values are reported, allowing a comprehensive characterization of both central tendency and data dispersion under each experimental condition. The means varied moderately among the irradiated groups, with higher values observed in the 40.7 Gy/h and 1 Gy/h treatments compared with the control. However, this variation does not follow a strictly monotonic pattern as a function of dose rate, suggesting a non-linear biological response to irradiation when bacteria are maintained in liquid suspension. The combined analysis of standard deviation and interquartile range indicates heterogeneity in variability among groups. The greatest dispersion is observed in the 20.1 Gy/h and 40.7 Gy/h treatments, which may reflect more diverse cellular responses under these conditions. In contrast, groups exposed to intermediate and low dose rates show lower variability, suggesting greater phenotypic uniformity. The coefficient of variation ranged from 28.02% to 56.11%, indicating moderate variability among treatments.
Descriptive statistics (mean, median, standard deviation, and interquartile range) of experimental values obtained for bacteria irradiated in suspension under different dose rates (Gy/h), including the non-irradiated control group.
Table IX summarizes the descriptive statistics of bacterial groups irradiated in the biofilm form and exposed to the same dose rates applied to the suspension groups. The reported parameters allow evaluation of how biofilm structural organization influences the quantitative response to ionizing radiation. Overall, a progressive reduction in mean and median values is observed as the dose rate decreases, particularly when compared with the control group. Unlike what is observed in suspension, biofilm groups exhibit mean values consistently lower than the control across nearly all irradiated conditions, suggesting increased sensitivity or distinct metabolic responses in this growth mode. Data dispersion, assessed by standard deviation and interquartile range, is lower in biofilm groups compared with suspension, particularly at lower dose rates. This indicates a more homogeneous response of the bacterial population in biofilm, possibly associated with structural protection and the collective physiology characteristic of this growth state. The coefficient of variation ranged from 22.74% to 40.50%, indicating moderate variability among treatments.
Descriptive statistics (mean, median, standard deviation, and interquartile range) of experimental values obtained for bacteria irradiated in biofilm under different dose rates (Gy/h), including the non-irradiated control group.
Table X presents the results of the non-parametric Kruskal–Wallis test, applied to evaluate overall differences among experimental groups irradiated in suspension and in biofilm. This test was selected because it does not assume data normality and is therefore appropriate for asymmetric or heteroscedastic distributions. For suspension groups, the test yielded a high χ² value (19.7) associated with a p-value of 0.0014, indicating statistically significant differences among at least two dose-rate groups. This result confirms that irradiation at different intensities measurably influences the analyzed variable under suspension conditions. Even more robust results were observed for biofilm groups, with a χ² value of 40.046 and an extremely low p-value (1.462 × 10⁻⁷). This finding demonstrates highly significant differences among the tested conditions, reinforcing that dose rate exerts a pronounced effect on bacterial responses in biofilm and justifying subsequent post hoc analyses.
Results of the Kruskal–Wallis test for global comparison among bacterial groups irradiated in suspension and in biofilm under different dose rates, including p-values, degrees of freedom (df), chi-square statistic (χ²), and statistical interpretation.
Aligned Rank Transform ANOVA revealed a significant overall effect of radiation dose and bacterial growth condition on the measured response (F(10, 352) = 8.92, p < 0.0001). This indicates that the response differs among at least some combinations of dose and condition. The global null hypothesis, stating that there is no difference between groups, is therefore rejected, confirming that radiation and/or growth conditions have an impact on the measured response. This global result demonstrates that variation in the response variable cannot be explained by random variability alone and justifies further pairwise comparisons to identify which specific condition–dose combinations account for the observed differences.
Pairwise comparisons using Bonferroni-adjusted contrasts identified which specific groups differ significantly. For example, biofilm exposed to 1 Gy differed significantly from suspension at 0 Gy (estimate = 118.21, p < 0.0001), and biofilm at 1 Gy differed from suspension at 40.7 Gy (estimate = -160.50, p < 0.0001). In contrast, some low-dose comparisons, such as suspension 0 Gy vs suspension 1 Gy (p = 1.0) and biofilm 1 Gy vs biofilm 6 Gy (p = 1.0), were not significant, and many comparisons between neighboring doses or between biofilm and suspension at identical doses, were not statistically significant. Overall, these results indicate that the observed differences are not uniformly distributed across all treatments but occur in specific condition–dose combinations.
These results show that the response is both dose- and condition-dependent, with stronger effects at higher doses or when comparing different conditions.
Overall, the data reject the null hypothesis that radiation dose and bacterial growth condition have no effect on the measured response. While low doses within the same condition may not produce statistically significant changes, higher doses or comparisons across conditions consistently show significant effects. This demonstrates that the bacterial response is both dose-dependent and condition-dependent, with thresholds below which radiation has minimal detectable impact.
Suspension Groups
For the groups tested in suspension, the control group exhibited intermediate mean and median values, with dispersion (SD and IQR as shown in Table VIII) comparable to most exposure groups and lower than that observed at the highest radiation doses, serving as a reference for evaluating exposure-related effects. The mean and median in the 40.7 Gy/h group are the highest (mean = 0.4435758 and median = 0.390), indicating a possible increase in bacterial growth in suspension with a high dose rate. Intermediate conditions (20.1 Gy/h and 10 Gy/h) show lower but close averages, suggesting a moderate response. The lower dose rates (6 Gy/h and 1 Gy/h) behave more closely to control.
The dispersion increases at higher dose rates (40.7 Gy/h and 20.1 Gy/h), possibly indicating greater variability in the biological response under extreme conditions. In contrast, the lower dose rates (6 Gy/h and 1 Gy/h) exhibit less scatter, suggesting more consistent responses.
The control group has a mean dose rate close to 1 Gy/h, indicating that very low dose rates can have similar effects to baseline. Higher dose rates, especially 40.7 Gy/h, show significant deviations from the control, suggesting greater radiation impact. Lower dose rates such as 6 Gy/h and 10 Gy/h showed similar means and medians, suggesting a possible response threshold.
The statistical analysis of suspension groups exposed to variable dose rates revealed significant differences between certain experimental conditions. The Kruskal-Wallis test revealed a statistically significant difference among the suspended group treatments (p=1.462 × 10⁻⁷), indicating that at least one group differed in distribution from the others. Post hoc Dunn’s tests with both Holm and Benjamini-Hochberg (FDR) adjustments were conducted to identify specific pairwise differences. Using the Holm method, significant differences were found between groups 2 and 3 (p = 0.0121) and between groups 2 and 5 (p = 0.0053), suggesting that these pairings had meaningful differences in their values even under a stricter control of Type I error.
The Benjamini-Hochberg (FDR) correction revealed additional significant pairwise differences, identifying differences between groups 2 and 3 (p = 0.0065), 2 and 4 (p = 0.0311), 2 and 5 (p = 0.0053), 3 and 6 (p = 0.0485), and 5 and 6 (p = 0.0405). These results indicate a broader pattern of significant group-level variation when a more permissive multiple testing correction is applied. Overall, group 2 showed consistent differences with several other groups, particularly with groups 3 and 5, highlighting it as a key source of variation within the suspended treatment group.
No monotonic association was detected between gamma radiation dose and bacterial metabolic activity in suspension, as indicated by Spearman’s rank correlation (ρ = 0.09, p = 0.87). Bootstrap analysis based on 9,999 replicates yielded a 95% confidence interval spanning the entire possible range (–1.00 to 1.00), indicating high uncertainty and providing no evidence for a dose-dependent relationship.
Cliff’s Delta analysis indicated predominantly small to negligible effect sizes across the evaluated dose-rate conditions. A small positive effect was observed at 40.7Gy/h (δ = 0.263; 95% CI: –0.014 to 0.502); however, the confidence interval marginally overlapped zero, suggesting a weak and potentially inconsistent effect. Similarly, treatment 20.1 Gy/h exhibited a small negative effect size (δ = –0.224; 95% CI: –0.480 to 0.067), while treatment 6 Gy/h also showed a small negative effect (δ = –0.264; 95% CI: –0.496 to 0.004), with confidence intervals encompassing or closely approaching zero.
Negligible effects were detected for treatments 10 Gy/h and 1 Gy/h, with delta estimates of –0.108 (95% CI: –0.347 to 0.143) and 0.112 (95% CI: –0.126 to 0.338), respectively. Overall, the overlap of confidence intervals with zero across all comparisons indicates substantial distributional overlap between groups, suggesting the absence of strong or consistent effects under these dose-rate conditions.
Biofilm Groups
For biofilm groups, analysis based on mean, median, standard deviation (SD) and interquartile range (IQR), as shown in Table IX, can provide initial insights into the effects of different conditions. The control group presents relatively intermediate values for mean and median, with a moderate dispersion (SD and IQR). It serves as a reference to evaluate the effects of different exposures. The 40.7 Gy/h group presents the highest mean value, suggesting a possible increase in biofilm. The high dispersion (SD and IQR) indicates variability in the results. In the 20.1 Gy/h group, there is a decrease in the GC, especially in the median. The dispersion is similar to the control but slightly higher (SD). For 10 Gy/h the values are similar to the GC in the median, but a slight reduction in the mean. The dispersion is smaller, indicating consistency in the data. The 6 Gy/h group presents the lowest mean and lowest dispersion, suggesting a possible uniform reduction in biofilm formation. And, for 1 Gy/h, results are close to the GC, with higher values in the median. The dispersion is low, indicating consistency in the data. From these initial interpretations, it is observed that in the 40.7 Gy/h group there is a possible stimulating effect of radiation on biofilm formation at high dose rates. The 20.1 Gy/h, 10 Gy/h, and 6 Gy/h groups show a gradual reduction in mean and median, suggesting a possible inhibitory effect as the dose rate decreases. For 1 Gy/h and the control group, the values are close, suggesting that very low doses do not significantly affect biofilm formation.
A Kruskal-Wallis test was conducted (p=1.462 × 10⁻⁷) to compare six groups in biofilm, revealing a highly significant difference among them. Post hoc Dunn’s tests using both Holm and Benjamini-Hochberg (FDR) corrections confirmed several statistically significant pairwise differences. Notably, the control group and group 40.1 Gy/h differed significantly from Groups 20.1, 6, and 1 Gy/h across both correction methods. Additionally, Group 10 Gy/h showed a significant difference from Group 1 Gy/h under the FDR correction. These consistent findings suggest that certain groups—particularly Group 1 Gy/h—exhibit distinct responses to the experimental conditions.
Biologically, the results suggest that Group 1 Gy/h is the most affected or responsive, showing strong differences from multiple other groups. This suggests that the condition applied to Group 1 Gy/h may lead to marked biological changes. Groups 20.1 and 6 Gy/h also demonstrated notable differences, especially when compared with the control or baseline groups (control and 40.1 Gy/h), pointing to potentially dose-dependent or treatment-specific effects. These findings highlight the importance of further examining the experimental conditions associated with the 20.1 Gy/h, 6 Gy/h, and particularly the 1 Gy/h groups, as the distinct responses observed under these dose rates may help guide future investigations aimed at elucidating the mechanisms underlying these differences.
No monotonic relationship was observed between gamma radiation dose and bacterial metabolic activity in suspension, as indicated by Spearman’s rank correlation (ρ = −0.03, p = 0.96). Bootstrap analysis based on 9,999 replicates produced a 95% confidence interval spanning the full range of possible values (–1.00 to 1.00), indicating substantial uncertainty and providing no evidence of a dose-dependent effect.
Cliff’s Delta was employed to assess the magnitude and direction of the effects across the evaluated treatments. The comparison at the highest dose rate (40.7 Gy/h) yielded a negligible effect size (δ = –0.022; 95% CI: –0.286 to 0.245), indicating no meaningful difference between groups. In contrast, several treatments exhibited negative effect sizes of varying magnitudes. A large negative effect was observed for treatment 20.1 Gy/h (δ = –0.477; 95% CI: –0.686 to –0.194), while treatment 1 also showed a large effect size (δ = –0.620; 95% CI: –0.802 to –0.331), reflecting a substantial reduction in the response relative to the reference condition.
Moderate to small negative effects were detected for intermediate treatments. Treatment 6 Gy/h presented a medium effect size (δ = –0.420; 95% CI: –0.636 to –0.142), whereas treatment 10 Gy/h showed a small but statistically supported effect (δ = –0.324; 95% CI: –0.553 to –0.050). Notably, except for the 40.7 Gy/h condition, the confidence intervals for these treatments did not include zero, indicating consistent directional effects and reinforcing the presence of biologically relevant differences among the dose-rate conditions.
DISCUSSION
A critical consideration in experiments involving ionizing radiation is the potential confounding effect of exposure time. Because irradiation necessarily entails handling and incubation outside standard growth conditions, temporal effects such as culture aging or transient physiological stress could, in principle, contribute to the observed responses. This possibility was addressed through the use of time-matched simulated irradiated controls, which underwent identical handling, incubation time, and environmental conditions without radiation exposure. Under a purely temporal explanation, irradiated and control groups would be expected to display comparable responses. Instead, the presence of structured dose and dose-rate-dependent effects, including non-linear trends and threshold-like responses, supports a radiation-specific biological contribution rather than a generalized time effect. Total dose was held constant while dose rate varied and differences in total dose rate necessarily result in different total irradiation times. As such, the present experimental design does not fully distinguish the effects of dose rate from those of exposure duration. Therefore, the observed responses should be interpreted as reflecting the combined influence of these factors, and not as definitive evidence of an isolated dose-rate effect.
Importantly, non-linear responses to dose rate are well documented in microbial radiobiology and further support a radiation-specific mechanism. For instance, an inverse dose-rate effect has been reported, in which extremely low dose rates (approximately 0.5 Gy/h) can induce greater biological damage than higher dose rates, resulting in a parabolic response pattern. This phenomenon has been attributed to insufficient activation of DNA repair systems at very low dose rates, where radiation-induced damage may not exceed the threshold required to trigger effective repair pathways. As a consequence, damage may accumulate more efficiently under prolonged low-rate exposure despite comparable or even lower total dose delivery (Min et al. 2003).
In addition, differential responses observed under conditions with equivalent exposure times but distinct dose rates further argue against time as the primary driver of the observed effects. Such dissociation between exposure duration and biological outcome is incompatible with a purely chronological explanation and is consistent with established radiobiological principles, in which damage accumulation, repair kinetics, and adaptive responses depend on dose delivery rather than elapsed time alone.
Moreover, differences in biological response as a function of radiation delivery modality further reinforce the importance of dose deposition dynamics over exposure duration. For example, studies comparing high-dose-rate electron beam irradiation (Linac) with gamma irradiation from Cobalt-60 irradiation have demonstrated substantially increased sensitivity under rapid dose delivery, even in highly radioresistant organisms such as Deinococcus radiodurans. These findings highlight that cellular outcomes are strongly modulated by the kinetics and quality of radiation exposure, rather than by elapsed time alone (Bruckbauer et al. 2020).
Another important interpretative issue concerns the use of the MTT assay as a proxy for bacterial metabolic activity. While MTT reduction reflects cellular redox activity and is commonly employed as an indicator of viable cells, it does not directly measure reproductive capacity. This distinction is particularly relevant in radiation biology, where sublethal damage and transient metabolic hyperactivation may occur. To mitigate this ambiguity, the interpretation of MTT data was supported as a measure of metabolic activity, reflecting the presence of metabolically active cells and allowing discrimination between changes in metabolic state and true survival. Consistent trends across assays suggest that the observed MTT responses reflect plausible biological differences in metabolic activity under the conditions tested, rather than isolated artifacts. Although this study was not designed to directly investigate molecular mechanisms of radiation response, the phenotypic differences observed among dose-rate treatments are consistent with previously described stress-response and repair processes in bacteria. However, these interpretations should be regarded as hypothesis-generating, as no independent measurements were performed to discriminate among alternative mechanisms such as redox imbalance, activation of repair pathways, or broader physiological changes.
The significant differences observed among dose–condition combinations suggest that the measured response is influenced by radiation exposure rather than remaining constant across treatments. This pattern is inconsistent with the hypothesis that the observed variation results only from total exposure time or experimental handling, as these factors were comparable across treatments. Instead, the presence of significant contrasts between specific irradiated groups and controls suggests that radiation dose contributes to the observed differences in metabolic activity. An alternative explanation is that the response reflects methodological artifacts associated with the MTT assay, such as variations in biomass, metabolic state, or reagent penetration within biofilms. Although such factors cannot be completely ruled out, the dose-dependent patterns observed across several comparisons are consistent with a biological response. Nevertheless, the current experimental design does not allow definitive separation between biological and methodological contributions. Taken together, these results support the interpretation that the bacterial response is influenced by both radiation dose and growth condition, although additional assays measuring viability or biomass would further clarify the biological basis of the observed metabolic changes.
The results suggest that the null hypothesis that radiation dose has no effect on bacterial response can be rejected. The response is dose-dependent and condition-dependent, with higher doses generally producing stronger effects. However, low doses in biofilm show no statistically significant change, suggesting a possible threshold for observable effects.
The results provide evidence consistent with rejecting rival hypothesis A, which posited that observed effects were only due to total handling time or exposure rather than radiation. Significant differences between groups with identical handling but different radiation doses suggest that radiation, rather than manipulation time, contributed to the observed effects. Similarly, the data are not fully consistent with rival hypothesis B, which suggested that the effects could be artifacts of the assay (MTT measurement, biomass differences, or biofilm penetration). The consistency of dose-dependent responses across both biofilm and suspension conditions, as well as the alignment between global ART ANOVA and post-hoc contrasts, supports the interpretation of a biological contribution, although assay-related biases cannot be entirely ruled out.
While our results suggest significant differences in response between irradiated and non-irradiated groups, it is important to acknowledge that alternative explanations cannot be entirely excluded. Rival hypothesis A, suggesting that observed effects are only due to total handling or exposure time rather than radiation, is partially addressed by including non-irradiated controls at equivalent time points; nonetheless, minor contributions from handling cannot be completely excluded. Rival hypothesis B, proposing that the effects are artifacts of the assay, biomass differences, or limited penetration in biofilms, is mitigated by consistent trends across multiple doses and conditions, yet assay-specific biases may still play a role. Therefore, while our data strongly support a radiation-dependent effect, further experiments incorporating additional controls, such as independent viability assays, handling-only controls, or biofilm penetration measurements could help elucidate whether the resistance observed at low doses (as in 0.05 Gy) results from physical protection by the extracellular matrix or from a phenotypic state of metabolic dormancy strengthening the causal inference and fully address these alternative explanations.
Hypotheses for larger dispersion at low doses
At low radiation doses, increased dispersion may reflect heterogeneous adaptive responses rather than uniform cellular damage. Low doses of ionizing radiation can induce hormetic effects, acting as moderate stimuli that activate beneficial cellular processes such as enhanced DNA repair mechanisms and antioxidant defenses (Calabrese & Mattson 2017). However, these adaptive responses are not expected to be uniformly expressed across all cells. Different bacterial populations, and even individual cells within the same population, may respond differently depending on the efficiency of their cellular defense systems, metabolic state, and DNA repair capacity (Bruckbauer & Cox 2021), including stress responses associated with deviations from normal background radiation levels (Castillo & Smith 2017). Consequently, some cells may exhibit improved performance while others show limited or no response, leading to broader outcome distributions. If this mechanism predominates, increased dispersion should occur without a strong shift toward lethality, DNA damage markers should remain relatively low and weakly correlated with variability, and repeated low-dose exposures may reduce dispersion as adaptive responses become stabilized across the population.
Hypotheses for larger dispersion at high doses
At high radiation doses, increased dispersion is more plausibly explained by damage-driven selection rather than adaptive plasticity. High-dose ionizing radiation induces extensive DNA lesions, including single-strand breaks, double-strand breaks, and base alterations (Confalonieri & Sommer 2011, Vignard et al. 2013), as well as membrane damage (Stefani et al. 2024) and oxidative stress (Arts et al. 2015). Under these conditions, bacterial survival depends strongly on the efficiency of DNA repair mechanisms and on the type and intensity of radiation exposure (Joseph & Badrinarayanan 2020). Cells possessing robust repair systems and stress-defense pathways may survive and even maintain growth, whereas less resistant cells may experience inhibition or cell death (Minton 1994), resulting in highly heterogeneous outcomes. Accordingly, if selective damage is the dominant driver of dispersion, outcome distributions are expected to become asymmetric or bimodal, dispersion should correlate with markers of DNA damage and oxidative stress, and survivors isolated from high-dose treatments should exhibit increased resistance upon subsequent irradiation (Confalonieri & Sommer 2011).
Hypotheses for lower dispersion at intermediate doses
The lower dispersion observed at intermediate radiation doses, such as 5 and 10 Gy, may reflect a species-specific stress response in which radiation-induced stress is sufficiently strong to trigger coordinated adaptive mechanisms across the population, yet not severe enough to exceed the repair capacity of most microorganisms (Bruckbauer & Cox 2011, Lim et al. 2019). In this dose range, damage may remain largely repairable, promoting synchronized activation of stress-response and repair pathways and resulting in more homogeneous outcomes. These intermediate doses may therefore represent a threshold between sublethal stimulation and overt lethality, producing consistent effects across the population. If this interpretation is correct, intermediate doses should be associated with shifts in central tendency accompanied by narrower distributions, moderate but repairable levels of DNA damage, and reduced variance in stress-response markers compared with low-dose conditions.
CONCLUSIONS
The observed maintenance or increase in metabolic activity in E. coli at lower radiation doses suggests that sublethal exposure may promote adaptive or stress-response mechanisms rather than immediate loss of metabolic activity. This finding is particularly relevant in the context of space environments, where microorganisms are subjected to chronic low-dose radiation over extended periods. Under such conditions, even modest increases in stress tolerance or metabolic activity could contribute to microbial persistence and biofilm formation in spacecraft-associated systems. Although the higher doses evaluated in this study exceed typical spaceflight exposure levels, they provide insight into the limits of bacterial response and the potential for resilience under extreme or accidental radiation scenarios. Together, these results support the relevance of our findings for understanding microbial behavior in space habitats, while highlighting the need for further studies under more complex radiation fields.
Higher dose rates could potentially be associated with adaptive cellular responses or metabolic changes that favor a metabolic output; however, in the absence of direct mechanistic measurements, these effects should be interpreted as hypotheses rather than demonstrated results. For suspension cultures exposed to 6 and 10 Gy/h, the close agreement between mean and median values may suggest the presence of a response threshold beyond which cellular behavior becomes more uniform. This pattern aligns with the possibility of stress-response or DNA repair pathways being activated beyond a certain radiation intensity, such mechanisms were not directly assayed and remain as interpretative hypotheses.
Radiation exhibited a dose-dependent effect on biofilm formation, as reflected by the progressive increase in both mean and median values with increasing dose. Nevertheless, the wide dispersion observed at both low and high doses suggests that radiation does not elicit a uniform response across the population under these conditions. Instead, variability at low doses may reflect heterogeneous adaptive or metabolic responses among cells, whereas increased dispersion at high doses is more plausibly linked to a non-uniform decline in metabolic activity, which could be related to variation in individual cell damage or repair efficiency. However, as survival was not directly measured via colony-forming units, these trends should be viewed strictly as metabolic signatures of the population’s response to radiation.
This dose range may represent a regime in which radiation-induced effects are consistent enough to elicit a standardized metabolic response across the population. While it is tempting to attribute this to a balance between damage and repair capacity, this study’s design only allows for the description of the resulting metabolic stability, leaving the underlying molecular triggers to be confirmed by future mechanistic studies.
For the first assay, conducted under a constant dose rate, statistical analysis using the ART method revealed that the biofilm state provides significant protection at low radiation doses (up to 0.05 Gy), where metabolic reduction is masked or mitigated by the biomass structure, partially supporting Rival Hypothesis B. However, the hypothesis that the observed effects are merely artifacts of handling (Rival Hypothesis A) was rejected, given the clear dose-dependent effect observed in both sessile and planktonic cells. At higher doses (≥10 Gy), the physical barrier provided by the biofilm appears to be overcome, resulting in a convergence of metabolic responses between the two cellular states. For the second assay, conducted under a variable dose rate, analysis using the ART method revealed that cellular configuration (biofilm vs. suspension) is the primary factor driving variation in the response to radiation treatment, particularly at low and moderate doses. The significant difference between suspension at 1 Gy/h and biofilm at 1 Gy/h (p < 0.0001) corroborates previous literature indicating that biofilms require substantially higher doses to achieve the same level of metabolic reduction observed in suspended cells. The data also suggest the presence of an efficacy threshold. At a dose rate of 40.7 Gy/h, no statistically significant difference in metabolic response was observed between biofilm and suspension cultures (p = 1.0000). This may indicate that, at this intensity, the radiation-induced impact on metabolism is comparable across both phenotypes, potentially suggesting that the protective role of the extracellular matrix is less determinant under these specific conditions. Furthermore, the rejection of Rival Hypothesis A supports the interpretation that the observed metabolic shifts are primarily associated with radiation exposure, rather than confounding factors related to experimental handling. However, these results describe a convergence of metabolic states and should not be interpreted as a direct demonstration of matrix penetration or specific repair exhaustion.
Although the radiation conditions tested here do not directly replicate the complex radiation spectrum encountered in space, the observed responses of E. coli to different gamma dose rates contribute to the broader understanding of microbial resilience to ionizing radiation.
Taken together, these observations suggest that dispersion patterns across dose rates provide indirect evidence for a balance between adaptive plasticity, DNA damage repair, and metabolic survival. However, distinguishing between enhanced DNA repair activity, metabolic reprogramming, and selection of resistant subpopulations will require additional measurements, such as direct quantification of DNA damage, repair kinetics, oxidative stress markers, or metabolic profiling. These approaches are essential to substantiate the proposed mechanisms and to assess the relevance of radiation-induced responses for applications in radiation biology, microbial resilience, and biofilm control strategies.
SUPPLEMENTARY MATERIAL
Acknowledgements
We thank the Bioengineering Laboratory team at the Aeronautical Institute of Technology for their support in designing, conceptualizing, and reviewing this work. We would also like to show our gratitude to the Anaerobic Laboratory team at São Paulo State University Júlio de Mesquita Filho for providing the facilities for conducting the experiments in this work. This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) [finance code 001].
Data availability
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