Unlocking bacterial potential to reduce farmland N2O emissions

Robotized batch cultivations for respiratory phenotype

NNRB have attracted much interest recently as net sinks for N2O in soils, potentially curbing N2O emissions4,31. NNRB strains vary grossly in their apparent capacity to act as N2O sinks, assessed by determining their biokinetic parameters: NNRB strains are commonly assumed to be strong N2O sinks if they have strong affinity (low apparent Km) for N2O and a high maximal rate of N2O reduction (Vmax), or simply a high catalytic efficiency (that is, a high Vmax/Km)38. Another desirable, albeit speculative, feature would be to reduce N2O under oxic or at least hypoxic conditions53.

To assess Cloacibacterium sp. CB-01 along these criteria, we conducted in-depth investigations of its respiratory phenotype by batch culturing in the robotized incubation system designed and described previously54,55, with the OpenLAB CDS 2.3 software for GC data acquisition (Agilent). The system hosts up to 30 parallel stirred batch cultures (normally 50 ml) in 120-ml gas-tight serum vials (crimp-sealed with butyl rubber septa) with a He atmosphere (with or without N2O and O2), which are sampled frequently for measuring the concentrations of O2, N2, N2O, NO and CO2 in the headspace. Robust routines are established for calculating the rates of production and consumption of all the gases (taking sampling loss and leakage into account), and for calculating gas concentrations in the liquid as a function of measured gas concentrations in the headspace and the rate of transport between liquid and headspace. These routines are included in a spreadsheet that is publicly available, including a set of instruction videos56. The system has been used in numerous investigations of the respiratory phenotypes of denitrifying bacteria6,7,33,57,58,59,60,61,62.

To enable refined analyses of the respiratory phenotype of CB-01, we initially determined the cell dry weight (femtograms per cell), and the growth yields for aerobic (\({Y}_{{{\rm{O}}}_{2}}\), cells per mole of O2) and anaerobic (\({Y}_{{{\rm{N}}}_{2}{\rm{O}}}\), cells per mole of N2O) respiration by measuring the cell yields in batches provided with various amounts of O2 and N2O. This enabled inspection of the cell-specific respiration rates (fmoles per cell per hour) throughout subsequent batch incubations, based on measured rates (moles of O2 and N2O per vial per hour) for each time interval between two gas samplings, and the estimated cell number in the vial for the same time interval (=Nini + \({Y}_{{{\rm{O}}}_{2}}\) × cumO2 + \({Y}_{{{\rm{N}}}_{2}{\rm{O}}}\) × cumN2O, in which Nini is the initial number of cells at time 0, and cumO2 and cumN2O are the cumulated consumption of the two gases). The cell-specific rates calculated this way allowed an analysis of the affinity for O2 and N2O by plotting cell-specific rates of O2 and N2O against the concentrations of the two gases in the liquid as the cultures depleted the gases, and fitting the Michaelis–Menton function to these data (least squares). Batch cultures provided with both N2O and O2 in the headspace were monitored as they depleted O2 and switched to respiring N2O, thus determining the critical concentration of O2 (in the liquid) at which the cells started to respire N2O. The kinetics of electron flow throughout such transitions from aerobic to anaerobic respiration were used to assess the fraction of cells expressing N2O reductase in response to O2 depletion, using a simplified version of the model developed previously60.

All phenotype experiments were conducted at 23 °C. The medium used was GranuCult nutrient broth (product number 1.05443, Merck): 8 g l−1, containing meat peptone and meat extract, pH-adjusted to 7.3 with NaOH. Additional experiments were conducted with autoclaved digestate (aerated and pH-adjusted to 7.3, as described below).

Culturing CB-01 in digestate for field experiments

For each field experiment, fresh digestate was collected from a wastewater treatment plant close to Oslo (VEAS), described in ref. 6. Averaged values of the quality parameters for the period of digestate collection were: dry matter content = 3.97 wt% (s.d. = 0.16), ignition loss of dry matter = 55.6% (s.d. = 2), pH = 7.72 (s.d. = 0.07) and NH3 + NH4+ = 1.71 g N l−1 (s.d. = 0.12).

Before cultivation of CB-01, the digestate was heat-treated, aerated and pH-adjusted. For the field bucket experiments, the digestate was autoclaved (121 °C for 20 min), and then sparged with air (while stirred) for 48 h to secure chemical oxidation of Fe2+ to Fe3+, and then autoclaved again. Oxidation of Fe2+ by air sparging was considered necessary to avoid abiotic oxygen consumption, as the digestate had high concentrations of Fe2+ originating from the Fe3+ used as precipitation chemicals in the primary wastewater treatment, and reduced to Fe2+ in the anaerobic digesters6. The sparging caused the pH to increase to 9.4 owing to the removal of CO2, requiring a final pH adjustment to 7.3 (with HCl). The same procedure was used for the field plot experiment, except that autoclaving was replaced by heat treatment: 70 °C for 4 h.

CB-01 was then grown aerobically in the pretreated digestates, inoculated to an initial cell density of about 5 × 107 cells per millilitre, which were stirred and sparged with sterile air (filtered) at 23 °C. To monitor the growth of CB-01, we transferred subsamples of each batch (after inoculation) to 120-ml vials (50 ml per vial) with Teflon-coated magnetic stirring bars, which were placed in the incubation robot system for monitoring the O2 consumption (Extended Data Fig. 5a–c).

Field experiments

Emissions of N2O in all outdoor experiments were monitored by the ‘dynamic chamber’ technique52,63, operated by an autonomous field flux robot described previously64, and shown in detail in Supplementary Fig. 1.

Field bucket experiments

Soils for the bucket experiments were collected from agricultural fields in southern Norway, spanning a range of soil characteristics. The acid sandy silt soil (S) was taken from an agricultural field in Solør, Norway, dominated by fluvial sandy silt soils. The clay loam soils L, I and N were from different plots within a liming experiment near the Norwegian University of Life Sciences (59° 39′ 48.2″ N 10° 45′ 44.8″ E), limed in 2014 (ref. 41): the low-pH clay loam (L) received no lime, the intermediate-pH clay loam (I) was limed with 2.3 kg m−2 of dolomite, and the neutral-pH clay loam (N) was limed with 3 kg m−2 of finely ground calcite. Soil O was a clay loam soil from the same area as L, I and N (hence, with similar mineral components), but with a much higher content of organic C because it had been a wetland before cultivation. The soil characteristics are listed in Extended Data Fig. 6.

The soils used in the bucket experiments (S, L, N and O) were sieved (10 mm) in moist conditions and mixed thoroughly before filling into the buckets. The conically shaped buckets (height = 21.5 cm, top diameter = 23.5 cm, bottom diameter = 21.5 cm) had a total volume of 8.6 l. An approximately 1-cm layer of gravel (4–8 mm diameter) was placed at the bottom, covered with a nylon fibre cloth to prevent eluviation of the soil by drainage. For soils S, L and N, 8 kg soil dry weight was filled into each bucket, packed by thumping the bucket on the ground until the soil had reached a bulk density of 1 kg l−1. For the organic-rich clay loam soil, each bucket was filled with only 5.92 kg soil dry weight, reaching a bulk density of 0.74 kg l−1 after being packed to 8 l. The soil surface area of the buckets was 0.043 m2.

To secure equal initial amounts of NO3 m−2 for all soils, we mixed an amount of KNO3 to each soil to reach a level of 12 g N m−2 soil surface = 516 mg NO3-N per bucket (soil surface area = 0.043 m2). Digestate (480-ml per bucket = 11 l m−2 soil surface area) was mixed into the top ≈10 cm of the soil by ‘harrowing’, using a small hand-held rake. We used autoclaved digestates in which CB-01 had been grown to about 6 × 109 cells per millilitre, and as the control treatment we heat-treated this digestate (70 °C, 2 h), which effectively killed the CB-01 cells (tested by measuring respiration, results not shown). As an additional control treatment, buckets received water alone. The density of CB-01 cells per soil surface area immediately after application was 6.6 × 1013 cells m−2. The cell density in the upper 10 cm of the soil was about 6 × 108 cells per gram of soil dry weight for the soils S, L and N (bulk density = 1 kg l−1), and about 8 × 108 g−1 for soil O.

The buckets were placed on 1-m2 Plexiglass plates (1.5 mm), to avoid gas exchange with the soil below. The soil moisture (volumetric water content, m3 m−3) and temperature (°C) in the upper 5.5 cm of the soil were monitored by four Teros 11 sensors, connected to an EM50 logger (Meter Group). Emissions were measured by field flux robot, lowering the chambers over the buckets (Supplementary Fig. 1g).

In the first bucket experiment, using only soil N (Extended Data Fig. 6), starting on 14 July 2021, ryegrass (L. perenne) was sown the day after the incorporation of the digestate, and the emissions were monitored for 90 days. Within this time span, we added 200 ml autoclaved and pH-adjusted digestate (4.6 l m−2) without CB-01 three times (after 19, 33 and 89 days), to induce transient bursts of N2O emission. By the end of each burst of N2O emission induced by applying digestates, the upper 10 cm of the soil was sampled with an auger (diameter 1 cm) and stored in the freezer (−4 °C) until DNA extraction and subsequent molecular work. The auger was washed and sterilized with 70% ethanol between each sampling.

In a follow-up bucket experiment, all soils were included and monitored for 10 days, with no re-fertilization. Soil sampling was carried out after the first peak of N2O emissions, as described for the 90-day bucket experiment.

The digestate application’s influence on soil pH was tested in the laboratory by mixing soil with the same type and amount of digestate as applied to the 0–10-cm soil layers of the field buckets (0.11 ml per gram of soil) ±50% to show the potential pH in pockets with higher or lower than average concentration of digestate. Water was added (if needed) together with digestate to reach the same water-filled pore space (%) as in the field bucket experiment. The most prominent increase in soil pH was seen in the sandy silt soil (Extended Data Fig. 6), reflecting its low buffer capacity due to low content of clay and organic material (Extended Data Fig. 6), both known to be crucial factors determining the buffer capacity of soil65.

Field plot experiment

We established small (0.5 m2) test plots within larger field plots (8 m × 3 m) of a soil liming experiment (limed in 2014) on clay loam soil41,66 and re-limed with 174 g dolomite per square metre in 2019. We used the plots with soil I (Extended Data Fig. 6) that were previously limed with dolomite to pH(CaCl2) = 6.13 (s.d. = 0.10), and within each of the six replicate plots, we established two 0.7 m × 0.7 m test plots side by side (distance = 30 cm), fertilized with autoclaved digestate in which CB-01 had been grown to a cell density of about 6 × 109 cells per millilitre. We applied 4.5 l digestate per plot (= 9 l m−2), which was mixed into the upper ≈10 cm of the soil by a hand-held cultivator. The initial density of CB-01 was 5.4 × 1013 cells per square metre. If distributed throughout the soil layer that was sampled for analyses (0–10 cm depth = 125 kg soil dry weight per square metre, assuming a bulk density of 1.25 kg l−1), the initial cell density in the soil would be 4.3 × 108 cells per gram of soil. Soil samples for determining CB-01 abundance were taken from each plot (three replicate samples) before incorporation of digestate with CB-01, 9 days later, and after 10 months. The soil samples were stored in the freezer (−20 °C) until DNA extraction and following quantification by PCR.

The 0.5-m2 test plots were situated along the boardwalk for the autonomous field flux robot, which was used to monitor the N2O emissions (Supplementary Fig. 1f).

Calculations of emissions and statistical analyses

From the slope of the N2O regression lines (Supplementary Fig. 1e), the flux of N2O is calculated by the equation

$${q}_{{{\rm{N}}}_{2}{\rm{O}}}=\frac{{10}^{-6}\,ahp}{RT}$$

in which \({q}_{{{\rm{N}}}_{2}{\rm{O}}}\) is the flux of N2O (mol m−2 s−1), a is the slope of the regression line (ppm s−1), h is the height (that is, the volume divided by the ground surface area) of the chamber (m), p is the pressure (Pa), R is the universal gas constant (J mol−1 K−1) and T is the temperature (K).

For graphic presentation of the emissions, we used the Gaussian kernel smoother67 to plot floating averages for each treatment (solid curves) together with individual measurements (as dots; Figs. 2, 4 and 5).

Cumulated N2O emissions over a period of time are approximated by using the trapezoidal rule on the estimated fluxes \((\int {q}_{{{\rm{N}}}_{2}{\rm{O}}}(t){\rm{d}}t\approx \sum ({q}_{{{\rm{N}}}_{2}{\rm{O}}}\left({t}_{i}\right)+{q}_{{{\rm{N}}}_{2}{\rm{O}}}\left({t}_{i+1}\right))({t}_{i+1}-{t}_{i})/2)\). This was carried out for each individual bucket and field plot.

The field plot experiment yielded paired data—six pairs (Xi, Yi), i = 1 … 6, in which Xi are cumulated emissions from plots treated with NNRB, and Yi are cumulated emissions for control plots. This gives six ratios Ri = Xi/Yi. Confidence intervals for the mean of the ratios, 1/6 ΣRi, for two time periods were made with a Student’s t distribution (assuming that the ratios were normally distributed). These confidence intervals were similar to confidence intervals found by the Fieller method for ratios of paired data and also by simple nonparametric bootstrapping68.

As the field bucket experiments did not yield paired data, flux reduction statistics are calculated as ratios of means, rather than means of ratios, of cumulated fluxes. Confidence intervals of these ratios were made by the Fieller method for unpaired data69 and by simple nonparametric bootstrapping (the results were similar). The 95% coverage of the Fieller confidence intervals was tested by numerical simulations and a bootstrap-calibration of the confidence level was made, with negligible effects on the confidence intervals.

The plots in Figs. 2, 4 and 5 were prepared using the packages Tidyverse (v2.0.0)70, Pracma (v2.4.2)71, ggbreak (v0.1.2)72, patchwork (v1.1.3)73 and scico (v1.5.0)74, in the R Studio software (v4.3.2)75. Colours used in the figures are, in general, from the scientific colour maps as described in ref. 76. The Fieller and bootstrap confidence intervals were calculated using Python (v3.11.5)77 with Scipy (v1.11.2)78 and Pandas (v2.1.1)79, and Julia (v1.9.3)80.

Tracing CB-01 in digestate and soil

To quantify CB-01 cells in digestate and soil, we used qPCR with primers specific to members of the genus Cloacibacterium developed previously81. The primers 5′-TATTGTTTCTTCGGAAATGA-3′ (Cloac-001f) and 5′-ATGGCAGTTCTATCGTTAAGC-3′ (Cloac-001r) target a region of the 16S rRNA gene.

DNA was extracted with the DNeasy PowerSoil Pro Kit (Qiagen) according to the manufacturer’s protocol, except for the first step: bead beating of the cells was carried out at 4.5 m s−1 for 45 s in a FastPrep-24 (MP Biomedicals), instead of a vortex. To measure the concentration of DNA in the extract, we used a broad-range or high-sensitivity Qubit dsDNA Assay Kit (Thermo Fisher Scientific), depending on the expected concentration. The number of CB-01 16S rRNA gene copies in extracted DNA was quantified using a CFX96 Touch Real-Time PCR Detection System (Bio-Rad), running for 15 min at 95 °C followed by 40 cycles of denaturation (30 s at 95 °C), annealing (30 s at 55 °C) and elongation (45 s at 72 °C). The final concentration of the master mix contained 0.2 µM of each primer (Cloac-001f and Cloac-001r), and 1× HOT FIREPol EvaGreen qPCR Supermix (Solis BioDyne).

For calibration, we used DNA-extracted suspensions of washed cells containing 103, 104, 105, 106, 107 and 108 cells per millilitre, resulting in 2.4 × 101–2.4 × 106 16S templates per PCR tube (taking dilution into account, and the fact that each genome of CB-01 contains three 16S rRNA genes). Results from the qPCR were analysed using the CFX Maestro 1.1 software (v4.1.2433.1219 from Bio-Rad). To enable the use of the Cq values to estimate copy numbers, we used the generalized reduced gradient solver in Excel to fit the model (equation (1)) to the data:

$$N=\,\frac{{N}_{T}}{{(2\times e)}^{{\rm{Cq}}}}$$

(1)

in which N is the initial number of 16S rRNA gene templates in the PCR tube, NT is the number of amplicons per tube needed for signal detection (above background), e is the efficiency of the PCR amplification and Cq is the number of cycles needed for detection of a signal. The fitted parameters were NT = 7.68 × 1010 copies per tube and e = 0.85 (85% efficiency).

An independent dataset was provided by running qPCR with the same primers on extracted DNA from suspensions of unwashed CB-01 cells (in nutrient broth) with densities 104, 105, 106, 107 and 108 cells per millilitre. The log10 values of cell densities estimated by the Cq values were on average 104% of the expected value, with a standard deviation of 6%.

When using qPCR to estimate the CB-01 abundance in soil and digestate, inhibition of the polymerase can result in too high Cq numbers, hence resulting in underestimation of the gene abundance82. To investigate this, we spiked the different soils and the digestate with 109 CB-01 cells per gram of soil dry weight and per millilitre of digestate, respectively, extracted DNA from 0.2 g soil and 0.2 ml digestate, and eluted to a 50-µl DNA solution for each material, which was then diluted in tenfold steps from 0 (undiluted) down to 1/107. The results show a reasonable fit between model (predicted) and measured Cq values for all materials if diluting the extracted DNA to ≤1/10, except for the intermediate-pH clay loam (pH(CaCl2) = 6.13), which required dilution to ≤1/100 to eliminate inhibition (for further details, see Supplementary Fig. 2).

The result was used to approximate the lower limit for detection of CB-01 in soils and digestate: a cautious upper limit for Cq values to be trusted is 40 (that is, 34 templates per PCR tube; equation (1)). The polymerases were evidently inhibited by using undiluted DNA in the reaction (Supplementary Fig. 2); hence, a 1/10 dilution of the extracted DNA is needed for all soils except soil I, for which 1/100 dilution is required. This means that the PCR tube can maximally be loaded with DNA from 0.8 mg soil (0.08 mg for soil I) and 0.8 µl digestate. This implies a limit of detection around 4.3 × 104 templates per gram of soil (4.3 × 105 for soil I owing to dilution to 1/100) and per millilitre of digestate, or 1.4 × 104 CB-01 genomes per gram of soil and per millilitre of digestate (as the genome contains three copies of the 16S rRNA gene).

The real limit of detection for a CB-01 inoculum in soil and digestate could be higher than this, if indigenous genes are amplified with the primers. This was tested by running PCR on soil and digestates that had not been spiked with CB-01, along with analysing spiked samples in various experiments. The results are summarized in Supplementary Fig. 2. As there were several tubes with a negative result (Cq > 40), average values cannot be calculated. A cautious judgement would be that the ‘background’ PCR signal of the soil is Cq = 39–38, which is equivalent to 67–107 templates per PCR tube, or 21–36 CB-01 genomes per tube. For all soils except I, we used the Cq values for the PCR tubes loaded with 1/10 dilutions, which were thus loaded with DNA from 0.8 mg soil. For these, the background PCR signal is equivalent to 2.6–4.8 × 104 CB-01 genomes per gram, and 10 times higher for soil I (owing to 1/100 dilution of the DNA from this soil). For digestate, the average Cq was 31.98 (Fig. 2), which means that the untreated digestate contains 3.2 × 106 CB-01 16S templates per millilitre, or 1.1 × 106 CB-01 genomes per millilitre.

Survival of CB-01 in soil

Laboratory experiment

A soil incubation experiment was designed to assess the survival of CB-01 in soil, vectored by digestate, under constant temperature and moisture conditions, and without any subsequent incorporation of digestate (thus contrasting with the field bucket experiment, Fig. 2). CB-01 was first grown to about 6 × 109 cells per millilitre in autoclaved, aerated and pH-adjusted digestate (as for the field experiments). Neutral-pH clay loam soil (soil N, see Extended Data Fig. 6) was portioned into a set of 50-ml Falcon tubes (9.4 g soil dry weight, moisture content = 0.5 ml g−1 soil dry weight). To each tube, 4.2 ml sterile water and 0.85 ml digestate (with CB-01) were dripped onto the soil. The tubes were stored in a dark moist chamber at 15 °C, with loose lids to allow exchange of air. Control tubes received only sterile water. At intervals, two replicate tubes were frozen (−20 °C) for quantification of CB-01 16S rRNA gene abundance by qPCR as described above.

Field plot experiment

From each individual plot (Fig. 5) we took three replicate soil samples, 9 and 280 days after fertilization, for quantification of CB-01 abundance by qPCR.

Extrapolating to national emission reductions

We use the emissions quantified with the GAINS model48,49 for 2030 in Europe to estimate the possible reductions of the measure.

The experiments described in this paper demonstrate marked emission reductions on all soils tested, over extended periods. The strongest reductions have been seen for the initial N2O peak immediately after fertilization, but NNRB has shown to remain active over a period of 90 days. Cumulated emissions over the whole period have been reduced by at least 41% (for clay loam soils), up to 95% reduction. We may disregard the case of the smallest reduction as the emissions from these soils are also rather small, but the organic loam soils (55% reductions) need to be considered. Consistent with the uniform emission factor used in GAINS (from IPCC50) of 1% of N applied to be emitted as N2O for all conditions of crops, soil or type of fertilizer added, a uniform reduction factor of 60% of emission reductions due to NNRB, which we consider a conservative estimate, was also applied. In Extended Data Table 3, emission reductions are shown by European country for 2030 if emissions from application of liquid manure alone are reduced by 60%. This assumption is based on the understanding that liquid manure can easily be treated in biodigesters. The authors of ref. 83 assume, for the purpose of methane abatement, that anaerobic digestion becomes profitable only for large agricultural entities of at least 100 livestock units. According to GAINS numbers, this concerns 70% of all farms in Europe, which more probably reflect liquid rather than solid manure systems, so the above estimate remains valid for the main fraction of liquid manure available. Indirect emissions as well as other soil emissions due to grazing, mineral fertilizer additions or application of farmyard manure (solid manure systems) have been left unchanged. Note that the GAINS model (in agreement with IPCC50) does not account for potentially increased emissions due to dry periods or freeze–thaw cycles (the latter considered to potentially contribute as much as 17–28% to global soil emissions84) but it covers increased emissions from cropping histosols.

Under these assumptions, total N2O emissions from Europe decrease by 2.7% owing to NNRB introduced. This figure is higher in countries that have a high share of liquid manure systems in their agriculture; hence, for EU27 (27 EU member countries) the corresponding figure is 4.0%, if NNRB were used for all manure nitrogen applied from liquid manure systems.

If it were possible to extend the NNRB technology, using solid manure and plant residues as substrates and vectors, we speculate emission reductions could be achieved for all mineral and natural fertilizer actively applied on fields. Ongoing work has shown that although Cloacibacterium sp. CB-01 grows to high cell densities in plant residues, new strains that grow in manure have been enriched and isolated (K. R. Jonassen and S. H. W. Vick, unpublished results). Although further development will be needed to implement this, it is relevant to estimate their impacts. Applying NNRB also to these other substrates at the same reduction efficiency could decrease European emissions as well as EU27 emissions by about a quarter (24% and 23%, respectively). For agricultural emissions alone, this means that roughly a third (31%) could be eliminated. For this calculation, we assume that indirect emissions from agriculture (due to re-deposition of ammonia released from fertilizers, or due to nitrate leaching), manure-management-related emissions and emissions from histosols remain unaffected.

It needs to be pointed out that an emission reduction of 60% as derived here for NNRB is much larger than emission reductions typically reported for N2O abatement measures. For example, GAINS assumes nitrification inhibitors to be able to reduce emissions by as much as 38%, and high-tech mechanical fertilizer-saving technologies (‘variable rate application’) to be able to save only 24% of the emissions48. Of note, the percentage reduction of N2O emission by the NNRB technology is plausibly unaffected by ‘variable rate application’ and nitrification inhibitor, as the target for NNRB is to reduce the N2O/N2 product ratio of denitrification, whereas the two others target the concentration of NO3 and nitrification, respectively.

Effect of CB-01 on the soil microbiome

Microbial community composition was examined by amplicon sequencing of the 16S rRNA gene V3–V4 region. Purified DNA from soil samples was sent to Novogene Europe for amplification, library preparation and sequencing to generate 250-base-pair paired-end reads using the Illumina Novoseq platform. Reads, after primer removal, were processed using GHAP (v2.4)85, an in-house amplicon clustering and classification pipeline built around Usearch (v11.0.66)86, the RDP classifier (v2.13)87 and locally written tools for generating operational taxonomic units (OTU) tables. Reads were processed using default quality control and trimming parameters. Clustering was carried out at both 97% and 100% similarity to generate OTUs and zero-radius OTUs (zOTUs), respectively. The 16S rRNA gene sequence of Cloacibacterium sp. CB-01 (GCA_907163125) was then matched against the OTU and zOTU representative sequences using the Usearch usearch_global command at 97% similarity and 99% similarity, respectively, to determine which OTU and zOTUs circumscribe the Cloacibacterium sp. CB-01 inoculant. From visual inspection it appeared that two zOTUs (zotu45 and zotu611) may circumscribe Cloacibacterium sp. CB-01 owing to shared abundance profiles and taxonomic classifications. To confirm that these two zOTUs both matched to Cloacibacterium sp. CB-01, the two representative sequences were BLAST-searched88 against the Cloacibacterium sp. CB-01 genome, and it was observed that both zOTU sequences matched closely to two separate regions of the genome, presumably harbouring multiple slightly divergent copies of the 16S rRNA gene. To confirm this, the two 16S rRNA genes from the Cloacibacterium sp. CB-01 genome were matched back against the zOTU representative sequences using the usearch_global command at 99% similarity, at which they matched to both zotu45 and zotu611, separately. Owing to this, zotu45 and zotu611 were combined for downstream analyses.

To assess the impact of the various treatments on the soil microbial communities, α- and β-diversity measures were calculated for microbial communities from all samples using the OTU tables generated above. OTU tables were first modified by removing the OTU circumscribing Cloacibacterium sp. CB-01 (OTU_27) before rarifying the tables to 72,846 reads per sample using the Usearch otutab_rare command. Shannon’s89 and Simpson’s90 diversity indices were calculated using the Usearch -alpha_div command and β-diversity measures were calculated using the Usearch -beta_div command. Jaccard’s dissimilarity measures91 were then used to generate multidimensional scaling plots using the Scikitlearn MDS module92.

The β-diversity as shown by Jaccard’s dissimilarity measures indicated that early during the soil incubation period there is greater between-sample variation both within treatments and between soils treated with live CB-01 and those treated with water or dead CB-01, indicating an effect of CB-01 on the soil microbial communities (Extended Data Fig. 7a). This effect, however, disappears by the final time point, at which samples from live-CB-01-, dead-CB-01- and water-treated soils cluster together, suggesting that the effect of live CB-01 on native soil microbial communities is transient and microbial soil communities are not affected in the longer term by the addition of live CB-01. It should be noted that the effect over time throughout the experiment is also a much larger source of microbial community variation than the addition of live CB-01 cells, presumably owing to disturbances to the soil from digging, sieving and packing of pots. Similarly, no systematic effects are observed on the α-diversity of soil microbial communities throughout the experiment indicating that the CB-01 treatment does not reduce the complexity or evenness of soil microbial communities when added to soils with digestate organic matter as can be seen in the Shannon and Simpson diversity measures of samples taken throughout the experiment (Extended Data Fig. 7b,c).

Search for antibiotics resistance genes and pathogenicity in CB-01

Microorganisms produce secondary metabolites crucial for diverse microorganism–microorganism interactions, enhancing survivability and competitive fitness through antagonistic effects on competitors under limited growth conditions. This array of metabolites, including antibiotics, toxins, pigments, growth hormones and anti-tumour agents, can also contribute to virulence and human pathogenicity. Such traits, if encoded in the inoculant’s genome, would restrict the use of such organisms as inoculants in agricultural soil. Likewise, the use of an inoculant would be restricted if its genome contains antibiotic resistance genes.

We checked CB-01 for such traits, scrutinizing its assembled draft genome7 in Pathogenfinder (v1.1)93 and ResFinderFG (v2.0)94, using standard settings. This revealed no evidence of human pathogenicity or antimicrobial resistance genes.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.


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