The antibiotic resistance crisis urges novel drugs against bacterial infections. Nucleic acids can serve as such drugs by inhibiting vital bacterial genes, provided their diffusion within bacterial cells is understood. We adapted PyFRAP, originally for eukaryotic cell protein diffusion analysis based on Fluorescence Recovery After Photobleaching (FRAP), to assess nucleic acid diffusion in bacteria. Validating the method, FRAP assays were conducted in E. coli expressing enhanced Green Fluorescent Protein (eGFP), yielding a diffusion coefficient (DC) of 1.25 (0.38) µm²·s⁻¹, consistent with the literature. We then investigated the internalization and diffusion of a 14-mer nucleic acid mimic (NAM) sequence in live bacterial cells, employing four fitting models. NAMs exhibited DCs (1.90 x 10⁻³ – 6.98 x 10⁻³ µm²·s⁻¹) aligning with theoretical values. This study validates PyFRAP for bacterial FRAP analysis, facilitating the understanding of nucleic acid diffusion and bacterial internalization and efflux mechanisms.
Article
Estimation of nucleic acid mimics (NAMs) diffusion in bacteria with fluorescence recovery after photobleaching (FRAP) and PyFRAP
https://doi.org/10.21203/rs.3.rs-4255877/v1
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Antimicrobial resistance
Nucleic acids
nucleic acid analogues
Fluorescence Recovery After Photobleaching (FRAP)
Diffusion
Escherichia coli
One of the most promising approaches in antimicrobial development are nucleic acid-based strategies1-3. These strategies typically resort to nucleic acid mimics (NAMs), which are chemically-modified nucleic acid chains, as they present higher affinity towards the target and resist degradation4. In fact, NAM-based therapeutics have already been FDA-approved for clinical applications in human cells 4-6. Moreover, the enhanced hybridization allows for shorter sequences which should increase the likelihood of bacterial uptake.
While the internalisation of nucleic acids has been extensively explored in eukaryotic cells7-10, the same is not true for bacteria. In fact, previous reports11-13 have suggested that live cells do not typically internalise nucleic acids, while others argue that internalisation of NAMs in bacteria may happen through currently unresolved mechanisms, depending on the specific bacteria and on the NAM's structure. For example, PS-LNA/2’OMe decamers have been reported to successfully target H. pylori cells5,14,15 and bactericidal phosphorodiamidate morpholino oligomers (PMO) could reduce the growth of E. coli in pure culture and in infected mice 16. In this sense, studying the processes behind the internalisation of NAMs inside bacterial cells becomes pivotal for their successful application as antibacterials or diagnostic agents.
Fluorescence Recovery After Photobleaching (FRAP) is the gold-standard technique to study reaction-diffusion behaviour on a micrometre scale17-21. FRAP takes advantage of fluorescently labelled molecules, tracking fluorescence fluctuations as a function of time22. A high-power excitation beam is employed to photobleach the molecules in a defined region of interest (ROI), followed by a low intensity light beam to monitor fluorescence recovery23. The intracellular diffusion of nucleic acids has been measured with FRAP, but these attempts have been limited to eukaryotic cells24-26 and extracellular matrices14. Importantly, the cytoplasmic viscosity is significantly different between mammalian and bacterial cells27, potentially impacting the diffusion of nucleic acids. In bacterial cells, FRAP studies have predominantly focused on GFP and GFP-fused proteins expressed through plasmids22,28,29.
PyFRAP is an established open-source software designed for the precise fitting of FRAP data and the calculation of diffusion coefficients (DC)30. One of PyFRAP's distinguishing features is its capacity to consider both influx and efflux during FRAP experiments. This attribute is particularly valuable, as it allows for the detection of events such as sustained internalization and efflux. However, PyFRAP was developed mainly as a FRAP analysis tool for living zebra fish embryos, which are significantly larger in size than bacteria (700 µm in diameter31 vs. 1 - 2 µm long32, respectively), as well as geometrically very different. Consequently, its application in bacteria lacks validation to ensure accurate determination of DCs.
In here, we investigated the diffusion of nucleic acids in the cytoplasm of Escherichia coli (E. coli). Keeping in mind future in vivo applications, the bacterial cells were not fixed nor permeabilized, as the reagents typically used for this purpose are toxic or noxious. As a proof of concept, a 14-mer NAM sequence, incorporating LNA, 2´-OMethyl RNA (2’-OMe) residues, and phosphorothioate (PS) linkages, and labelled with fluorescein, was used. By plotting the fluorescence intensity data over time, we derived DCs to extrapolate information regarding the internalisation process. Our study introduces the first experimental determination of the DC of a nucleic acid sequence in bacterial cells.
2.1. PyFRAP is suited for the study of diffusion in bacterial cells.
In order to validate PyFRAP in bacterial cells, we first measured the DC of eGFP in E. coli JM109. Fluorescent proteins, including GFP, yellow fluorescent protein (YFP), and red fluorescent proteins (RFP), (together with fusion proteins) are the only molecules for which diffusion in bacteria has been experimentally assayed22,33,34. A widely used GFP variant, eGFP, was selected due to its enhanced brightness and faster maturation compared to wild-type GFP35. A DC for eGFP similar to that of GFP can be reasonably expected, as eGFP only harbours two mutations, which have a minimal impact on both molecular weight and charge compared to its wild-type counterpart35-37. We started by tracking the fluorescence recovery of 11 elongated E. coli cells after photobleaching (Fig. 1a, Fig. S1, Supp. Video 1 - 11). In order to improve the reliability of the results, we selected a ROI in one of the poles of the cell (Fig. 1b), and also tracked the fluorescence across the entire cell (Fig. 1c). Using PyFRAP, results were fitted as described in Blässle et al.30 and considering a 2D geometry. An average DC of 1.25 (0.38) µm2·s-1 at an osmolality of 300 mOsm (Fig. 1d and Fig. S1) was obtained, consistent with previous reports indicating average diffusion coefficients of 1.8 μm2·s-1 (with values ranging from 0.1 - 13 μm2·s-1) in 370 mOsm buffer and 0.94 (0.55) μm2·s-1 in 392 mOsm buffer38,39. Importantly, the application of PyFRAP yielded a smaller standard deviation for eGFP's DC than what is typically reported (Fig. 1e), further confirming the suitability of PyFRAP for the analysis of FRAP datasets in bacterial cells.
2.2. NAMs diffusion patterns vary from cell to cell.
To assess nucleic acids diffusion patterns, NAMs in hybridization solution were added to bacteria trapped on an agarose pad, and E. coli K-12 cells were immediately visualized under a confocal microscope. NAMs were used because of their higher affinity towards DNA and RNA, meaning that shorter sequences, that in principle can cross the cellular envelope better, can be employed. Additionally, LNAs and 2´-OMe were preferred as they are negatively charged and as such more representative of the natural nucleic acid chemistries. This time non-elongated cells were tested, due to concerns over a change in diffusion due to the action of cephalexin in the cell envelope. Our observations revealed that a subset of the bacterial population demonstrated internalisation capability (Fig. 2a). In order to determine if these cells are viable, CTC staining was employed, which confirmed that a significant percentage of bacterial cells internalised by NAMs were actively respiring (Fig. 2b).
Sixteen FRAP datasets from three independent assays (Supp. Video 12 - 27), corresponding to the diffusion of a FAM-tagged 14-mer NAM sequence in the cytoplasm of E. coli cells that displayed fluorescence, were analysed with PyFRAP following a 2D interpolation (Fig. S2). Unlike for eGFP, that displayed overall similar fluorescence behaviours, three distinct patterns were identified for NAMs. In the first one, there is a full, stable recovery in the ROI, but overall fluorescence loss in the entire cell (Fig. 2c). This can be due to the fact that, because this time E. coli cells were not elongated as in the eGFP experiments, the bleaching of the ROI had more impact on the fluorescence of the entire cell. In the second pattern there is also an initial recovery of the fluorescence in the ROI, but a subsequent decrease in the overall fluorescence of the ROI and the entire cell (Fig. 2d). This decrease in the ROI and cell fluorescence hints not only to the impact of the bleaching in the fluorescence of the whole cell, but also to the possibility of NAMs efflux. Finally, in the third case there is an increase in fluorescence throughout the cell with recovery in the ROI surpassing the initial value (Fig. 2e). In this case, the recovery never reaches a steady state. This suggested a rapid and continued influx of NAMs during the imaging time.
These findings underscore the dynamic nature of NAMs’ diffusion and internalisation within specific bacterial cells. This variability in bacterial behaviour among isogenic lines has been documented previously 40. These results also highlight the critical importance of sample selection in such experiments to ensure accurate measurement of the diffusion of NAMs. This process involves carefully choosing datasets where the loss of fluorescence does not exceed a specific threshold, such that in- and efflux dynamics do not dominate diffusion dynamics. By optimizing the sample selection, we can minimize the likelihood of confounding factors and ensure the reliability of FRAP datasets.
To address the challenge of fluorescence stability and ensure the selection of appropriate datasets that are representative of NAMs’ diffusion kinetics, we further investigated the fluorescence stability by analysing neighbouring, non-bleached cells. Consistent with our earlier observations, three scenarios were observed: i) fluorescence loss over time (Fig. 2f), ii) maintenance of the fluorescence level (Fig. 2g), and iii) increase in fluorescence over time (Fig. 2h). As there is no bleaching in these cells, these data further support the existence of influx and efflux.
For each non-bleached, neighbouring cell, the examination of the relative difference between the maximum and minimum intensities recorded over the observation period (Supp. Table 1) unveiled a broad spectrum of values ranging from 13% to 54%. Once again, these scenarios imply photobleaching in certain cells and the continued influx of NAMs. Moreover, the observed fluorescence decrease points to the possibility ofefflux, as non-bleached cells should not suffer such high degrees of photobleaching, since they are not exposed to a high intensity laser beam.
Only three cells exhibited a mean relative intensity lower than 10%, indicating relatively stable fluorescence levels.
2.3. DCs obtained for NAMs are robust for selected datasets, regardless of the presence of Influx and Efflux Terms.
Given the high degree of variability in fluorescence stability, typical of a FRAP experiment, rather than treating all datasets equally, we opted for a careful and case-by-case approach to identify the most representative datasets for further analysis. It is generally accepted that a bleaching depth of 80% in the ROI is ideal, as long as there is minimum loss of fluorescence in the remainder of the cell41. Therefore, we established a threshold of 20% of acceptable fluorescence loss in the non-bleached portion of the cell. As such, we filtered out datasets with mean relative intensity higher than 20%, leaving us with a total of four samples suitable for further investigation. The selected datasets were subjected to four different types of fitting, considering: (A) pure diffusion, (B) influx, (C) efflux, and (D) both influx and efflux.
Sup. Fig. 1 presents the fitting obtained for each model, considering a 2D geometry. The DCs obtained with all four models (Table 1) fell within the same order of magnitude (2.10 x 10-3 – 7.27 x 10-3 µm2·s-1). The calculated DC values are also in good agreement with the theoretical DC reported in the study by Magalhães et al. for an 18-mer FAM-NAM sequence and calculated using the Stokes-Einstein equation (2.67 x 10-3 µm2·s-1)42.
Although 2D simplifications are commonly employed in FRAP analysis, considering complex geometries can be crucial for accurately understanding dynamic biological processes. In our study, in addition to fitting the data considering the cell as a 2D structure, we also followed a 3D interpolation of the cell (Fig. 3). Again, the DCs obtained with all four fitting models fell within the same order of magnitude (1.90 x 10-3 – 6.98 x 10-3 µm2·s-1). Interestingly, no significant differences (pvalue > 0.05) were found between the 2D and 3D approached for all fitting models (Fig. 4). This observation may be attributed to the absence of a full set of organelles, as found in eukaryotic cells and which could impact molecule diffusion, meaning that the viscosity and obstacles found in the cytoplasm are essentially the same in all directions 43. Therefore, the 2D approximation suffices for accurately analysing the diffusion of NAMs in bacterial cells.
Moreover, we also aimed to infer possible binding interactions between the NAM sequence and its target RNA, a ribosomal sequence in this case. To do this, mobile and immobile fractions should be identified on the FRAP recovery plots. However, the continuous increase or decrease in fluorescence prevented us from making such inferences. This highlights the need for further investigations to clarify the dynamics of NAM-target interactions in bacterial cells. Still, given the short observation timespan and consistency between the theoretical and experimental DCs, it is unlikely that a noteworthy fraction of NAMs has hybridized with the target ribosomal sequence. In such a scenario, there would have been a considerable decrease in the measured DC, attributable to the substantial molecular weight of the ribosome compared to the relatively smaller size of the NAM.
In this study, we started by demonstrating the suitability of PyFRAP for the analysis of FRAP datasets of bacterial cells, by measuring the DC of eGFP in E. coli and validating it against values previously reported in the literature. This approach established the applicability of PyFRAP for subsequent analyses.
The main focus of the investigation revolved around the diffusion of a 14-mer NAM sequence within the cytoplasm of E. coli. In our attempt to understand the internalisation of NAMs in live bacterial cells, we performed FRAP experiments and followed a comprehensive analysis methodology. We initially collected a total of 16 datasets. However, during the analysis, we encountered a high degree of variability in fluorescence stability across these datasets. This fluctuation was likely influenced by various factors, such as the extensive photobleaching associated with the reduced size of bacterial cells and continued influx of NAMs, demonstrated by the continuous increase of fluorescence observed in some datasets.
Notably, to our knowledge, this study presents the first experimental estimation of the diffusion coefficient of a nucleic acid sequence in bacterial cells. Furthermore, it highlights the importance of sample selection in FRAP experiments to ensure accurate measurement of NAMs' diffusion. By optimizing the selection process, confounding factors can be minimized, enhancing the reliability of FRAP datasets. Nonetheless, it is likely that in the future, superresolution microscopy can be employed to better estimate diffusion.
The findings presented here also contribute to a deeper understanding of NAMs as potential antibacterial drugs and offer valuable insights for future research in the field of antimicrobial resistance. As we continue to unravel the intricacies of NAMs’ diffusion and internalisation, we move closer to harnessing their full potential as a new generation of antibacterial therapies. While NAMs were able to cross the cell envelope of some cells unassisted, it is clear that to expedite the efficiency of NAMs internalisation, delivery vectors can be investigated to boost the percentage of internalised cells1.
4.1. Materials and Reagents
Reagents 2-amino-2-(hydroxymethyl)-1,3-propanediol hydrochloride (Tris-HCl), Triton X-100, Ethylenediaminetetraacetic acid disodium salt dihydrate (EDTA), sodium chloride (NaCl), and agarose low gelling temperature were purchased from Sigma-Aldrich (Bornem, Belgium). Formamide and 5-Cyano-2,3-ditolyl tetrazolium chloride (CTC) were obtained from VWR International. Tryptic soy agar (TSA), Tryptic soy broth (TSB), and Plate count agar (PCA) were purchased from Merck Millipore (Madrid, Spain). The NAM sequence was acquired from EUROGENTEC (Seraing, BELGIUM).
4.2. NAM Sequence
The sequence 5'6-FAM/lT*mG*mC*lC*mU*mC*lC*mC*mG*lT*mA*mG*lG*mA, where (FAM) represents fluorescein, l stands for LNA, m for 2´-OMe and * for PS internucleotide linkages, was used for this study. The sequence was complementary to a conserved 16S rRNA sequence in the domain Eubacteria 5. A stock solution of 500 µM was prepared in sterile ultrapure water and further diluted to 4 μM stock solutions. All stock aliquots were stored at -20 °C. The final concentration of 2 μM was achieved by diluting the 4 μM stock solution in hybridization solution (20% (v/v) formamide, 900 mM NaCl, 5 mM EDTA, 0.1% (v/v) Triton X-100, and 50 mM Tris-HCl) 5,44 and stored at 4 °C until use.
4.3. Bacterial Strains and Culture Conditions
Experiments were performed using E. coli JM109 (DE3) (Promega, Madison, USA) and E. coli K-12 MG1655. E. coli JM109 harboured the plasmid pFM23 for enhanced green fluorescent protein (eGFP) expression. This plasmid contained a kanamycin resistance gene for selection of plasmid containing bacteria 45. E. coli K-12 was cultivated on TSA 46, while E. coli JM109 was cultivated on PCA with kanamycin (20 µg/mL) 47. Both strains were grown overnight in TSB until the start of the stationary phase. E. coli K-12 was grown at 37 °C, whereas E. coli JM109 was grown at 30 °C, the optimal temperature for GFP internalization 48. Optical density (OD) at 600 nm was measured to guarantee that experiments would start with a similar initial concentration of bacterial cells (2.50E+10 CFU/mL).
Additionally, 1 mL of E. coli JM109 inoculum was centrifuged for 15 min at 16800 rcf in an Eppendorf Centrifuge 5418, and the bacterial pellet resuspended in fresh media. Two µL of 30 µg/mL cephalexin were added to the bacterial suspension of E. coli JM109, allowing it to grow for another 2 hours. Cephalexin, an antibiotic that blocks septation, elongates the cells, providing a larger non-bleached region that should contribute to fluorescence recovery after bleaching in the ROI and minimize overall photobleaching of the cell in FRAP experiments (described further ahead). The same antibiotic was not used for E. coli K-12 cells in the NAMs' diffusion assays to avoid compromising the wild-type bacterial envelope structure which could lead to an overestimation of NAMs diffusion. Finally, the inoculum of both strains was centrifuged for 15 min at 16800 rcf and the pellet resuspended in sterile NaCl saline solution at 0.15 M, corresponding to an osmolality of 300 mOsm (calculated by multiplying the sodium and potassium concentration by 2).
CTC was used to determine the respiratory activity of bacteria. CTC is a soluble crystal dye that competes with the final acceptor in the electron transport system. Respiring bacteria placed in CTC solution will take up the CTC and reduce it to insoluble formazan, which accumulates in the cells. On the other hand, dead or inactive cells show no accumulation of CTC formazan. To check for viability, 100 μL of 50 mM CTC were added to the saline bacterial suspension, gently vortexed and incubated for 30 minutes at 37 °C, protected from light.
4.4. Microslide Preparation
Agarose pads were prepared to function as bacterial traps and allow visualization of live bacterial cells without fixation. The solution was prepared by dissolving 2% low-melt agarose (w/v) in water. Thereafter, 60 μL were added on top of a glass slide and allowed to solidify at room temperature to a height of approximately one coverslip's thickness. A 5 μL drop of the saline bacterial suspension was then added onto the agarose pad. A coverslip was placed on top to spread the solution over the pad and left for 30 minutes for the bacteria to be immobilized in the agarose.
For the NAMs diffusion assays, the coverslip was removed, and a 5 μL drop of the 2 μM FAM-NAMs in hybridization solution was added to the agarose pad with trapped bacteria. This concentration of NAMs allows for a large surplus of probes when compared to the number of ribosomes in the cell 42. Cells in saline without the addition of NAMs served as negative control for autofluorescence. The samples were immediately visualized under a confocal microscope.
4.5. FRAP Microscope Setup
FRAP was performed on a Leica TCS SP8 confocal laser scanning inverted microscope equipped with an Argon Laser to measure the cytoplasmic DCs of eGFP and the 14-mer NAM. The sample was imaged with a PL APO 63x /1.40 oil-immersion objective and a pinhole of 1.0 Airy-disk units. The fluorophore or eGFP was excited using a 488 nm laser at 2% intensity, a speed of 1400 Hz, and a line average of 1 or 4 for E. coli JM109 expressing eGFP or E. coli K-12 cells, respectively. The microscope was enclosed in an environmental chamber and equipped with temperature control. The experiments with JM109 were performed at 30 °C, while those using K-12 were conducted at 37 °C, matching the bacterial growth temperatures. The same setup was used for the visualization of CTC-stained cells.
The ROI for FRAP was defined on one of the poles of the cell to ensure sufficient distance between the bleached region and the opposing pole. This arrangement reduces the effect of the intense bleaching laser on the fluorescent molecules in the latter area, facilitating subsequent fluorescence recovery. Five pre-bleaching images were taken, followed by a bleaching cycle of 10 frames with the 488 nm laser at 100% intensity, and 100 post-bleach images. Brightfield images were captured to identify the cells in which NAMs successfully internalised. FRAP movies were cropped using FIJI 49 to obtain individual movies for each cell, which were then analysed with PyFRAP 30 to derive the DCs.
4.6. PYFRAP Analysis
PyFRAP takes the first pre-bleach image and either perceives the cell in a two-dimensional (2D) geometry or interpolates it onto a three-dimensional (3D) mesh approximating the shape of the cell for numerical simulations of fluorescence recovery. The software also automatically detects the bleached area (i.e., the ROI).
4.6.1. Image analysis
Bacteria boundaries were automatically detected using Otsu thresholding 50, allowing for exact reinternalization of the bacterial geometry. Bleached ROIs were defined manually for each particular movie. After defining the ROIs, intensities were averaged across each ROI per timepoint:
Where r is the ROI of interest, and I(t,i) the intensity at pixel i and time t.
4.6.2. Fluorescence stability
To calculate the accurate DC of NAMs and ensure fluorescence stability, a pre-selection of datasets had to be undertaken. For this, cells were imaged in the same conditions described in the previous sections but skipping the bleaching step. Relative differences between maximum and minimum measured intensities for each dataset (d) were defined as:
Equation 1
where Ir (t) is the mean intensity in the ROI r at time t, t0 is the time of experiment start and T the final time point of the experiment.
Moreover, the mean relative intensity between start and end of the experiment (d ̃(r)) is given by:
Equation 2
Here, r represents a specific ROI, such as the complete bacterial cell or the bleached area, Δt denotes the time between two recorded frames, and n is the number of frames used to compute the means. Essentially, the mean intensity in a ROI over the last n = 20 timesteps is divided by the mean intensity in the first n time steps. Finally, 1 is subtracted from the results to extract the relative change. Values smaller than 0 indicate a decrease in fluorescence intensity at the end compared to the start of the experiment, while values larger than 0 suggest an increase in fluorescence over time. To further test if the data showed any significant trends, a Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test was applied to each dataset 51.
4.6.3. 3D geometrical model interpolation
To accurately estimate diffusion in a 3D geometry, we approximated the bacteria as 3D ovals. For that, we used the 2D boundary estimated using Otsu thresholding and assumed the diameter of the bacteria to be equal the maximum width of the bacteria.
4.6.4. Fitting models and geometry for FRAP datasets
We simulated both the 2D as well as the 3D geometry using reaction-diffusion models. To obtain a good approximation of the initial state of bleaching, we applied the first image of the post-bleach movie as the initial condition onto the 2D or 3D mesh. Since the bleached region experiences the largest dynamics during a FRAP experiment, we refined the mesh using an attractor mesh centered around the center of mass of the bleached area. We chose a finite element size ranging from 0.07 µm2 to 0.34 µm2 and ca. 0.4 µm3 to 1.6 µm3 for the 2D and 3D mesh, respectively. The attractor then refined the mesh down to 25% of finite element size in the bleached area, resulting in average 2788 and 1515 finite elements for the 2D and 3D geometries, respectively.
Simulation results were fitted using a constrained Nelder-Mead algorithm. To mimic continuous internalisation of the fluorescent NAMs or loss of fluorescence due to efflux, we fitted additional models adding a linear influx and/or exponential efflux term, adding to the diffusion model, ultimately resulting in four distinct models. We forced the model to capture both the recovery process as well as the overall intensity in the cell by fitting both the simulation results in the bleached area and complete cell to the image intensities.
Acknowledgments
We would like to thank Kevin Braeckmans, from the University of Ghent, for initial discussions about methods to assess diffusion in bacteria. This work was financially supported by: PhD Grant SFRH/BD/143491/2019 financed by Fundação para a Ciência e Tecnologia (FCT); UIDB/00511/2020 and UIDP/00511/2020 (LEPABE), and LA/P/0045/2020 (ALiCE), funded by national funds through FCT/MCTES (PIDDAC).
Credit authorship contribution statement
BTM: Methodology, Investigation, and Writing. AH: Formal Analysis and Writing, RSS: Conceptualization, Review and Editing, NFA: Supervision, Resources, Review and Editing, Project Administration.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Table 1. Average Diffusion Coefficients (DC) calculated following a 2D or 3D cell interpolation. Four fitting models were applied to each condition.
The four fitting models applied were: (A) simple diffusion without influx (I) and efflux (E) terms (I = 0, E = 0); (B) fitting with an efflux term (I = 0, E = 1); (C) fitting with an influx term (I = 1, E = 0); and (D) fitting with both influx and efflux terms (I = 1, E = 1).
|
Fitting Model |
Diffusion Coefficient [mean x 10-3 (SD) µm2·s-1] |
|
|
2D |
3D |
|
|
I = 0, E = 0 |
3.82 (0.0042) |
3.44 (0.0038) |
|
I = 0, E = 1 |
2.10 (0.0015) |
1.90 (0.0013) |
|
I = 1, E = 0 |
7.27 (0.0050) |
6.98 (0.0055) |
|
I = 1, E = 1 |
3.09 (0.0028) |
3.79 (0.0033) |
No competing interests reported.
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