Aimeric Blaud
email: a.blaud@sheffield.ac.uk

Tiphaine Chevallier

Iñigo Virto

Anne-Laure L Pablo

Claire Chenu

Alain A Brauman

Bacterial community structure in soil microaggregates and on particulate organic matter fractions located outside or inside soil macroaggregates

Keywords: microhabitats, coarse POM, fine POM, organo-mineral soil fraction, DGGE

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Soil can be considered a benchmark heterogeneous environment for microbial ecologists, as it is typically a complex environment comprised of a huge diversity of microhabitats. A number of studies examining this complexity have defined soil aggregates as specific soil compartments [START_REF] Mummey | Spatial stratification of soil bacterial populations in aggregates of diverse soils[END_REF][START_REF] Blaud | Dynamics of bacterial communities in relation to soil aggregate formation during the decomposition of 13 C-labelled rice straw[END_REF][START_REF] Davinic | Pyrosequencing and mid-infrared spectroscopy reveal distinct aggregate stratification of soil bacterial communities and organic matter composition[END_REF]. Several studies have shown that the different sizes of soil aggregates and locations within soil aggregates can select for different bacterial communities [START_REF] Ranjard | Heterogeneous cell density and genetic dtructure of bacterial pools associated with various Soil microenvironments as determined by enumeration and DNA fingerprinting approach (RISA)[END_REF][START_REF] Chotte | Changes in bacterial communities and Azospirillum diversity in soil fractions of a tropical soil under 3 or 19 years of natural fallow[END_REF][START_REF] Fall | Bacterial density and community structure associated with aggregate size fractions of soil-feeding termite mounds[END_REF][START_REF] Mummey | Spatial stratification of soil bacterial populations in aggregates of diverse soils[END_REF][START_REF] Blaud | Dynamics of bacterial communities in relation to soil aggregate formation during the decomposition of 13 C-labelled rice straw[END_REF][START_REF] Davinic | Pyrosequencing and mid-infrared spectroscopy reveal distinct aggregate stratification of soil bacterial communities and organic matter composition[END_REF]. Soil aggregates are formed by mineral associations with particulate organic matter (POM) via binding agents (e.g. fungal hyphae, plant roots, polysaccharides) [START_REF] Six | Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture[END_REF][START_REF] Six | A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics[END_REF]. Microaggregates (size < 200 µm) are formed within macroaggregates (size > 200 µm) and can be released from fragmented macroaggregates. Therefore, organic resources differ quantitatively and qualitatively between sizes and locations of aggregates [START_REF] Six | Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture[END_REF]. Moreover, POM has been shown to influence microbial community structure within the soil surrounding it, called the "detritusphere" [START_REF] Gaillard | Carbon, nitrogen and microbial gradients induced by plant residues decomposing in soil[END_REF][START_REF] Nicolardot | A microcosm experiment to evaluate the influence of location and quality of plant residues on residue decomposition and genetic structure of soil microbial communities[END_REF]. A study by [START_REF] Blackwood | Eubacterial community structure and population size within the soil light fraction, rhizosphere, and heavy fraction of several agricultural systems[END_REF] showed that rhizosphere and shoot residues are distinct bacterial habitats compared to other soil fractions including mineral particles and humified organic matter. However, there is still an intense debate about the potential role of soil aggregates in structuring microbial communities, and within these microhabitats little is known about the impact of POM quality and localisation on microbial community. Therefore, the aims of this study were to i) to determine whether organo-mineral (0-20 µm, 20-50 µm, 50-200 µm) and POM (coarse POM: > 200 µm and fine POM < 200 µm) soil fractions can represent distinct microbial habitats, and ii) to determine whether microaggregates and POM location, outside or inside macroaggregates (> 200 µm), can influence the bacterial community structure of these microhabitats. Henceforth, the term "organo-mineral soil fraction" is preferred to "soil aggregates" because this study did not separate soil aggregates from mineral particles.

A clayey Eutric Cambisol was sampled at the INRA-Epoisse experimental farm in Burgundy (France). The experimental field plots have been cultivated and tilled for 10 years with a rotation of wheat, rape, and barley. The soil texture was comprised of 11.2 % of sand, 41.8 % of silt and 47.0 % of clay. The organic C concentration was 26.8 g kg -1 , C;N ratio 12.4, pH (water) 7.8, CaCO3 3.2 g kg -1 and CEC 25.1 C mol kg -1 . Three soil cores (diameter, 7 cm) were randomly collected down to a depth of 30 cm, which represented the tilled layer of the soil (tilled annually), where the soil aggregates and POM are homogenised and fragmented. These soil samples were pooled to reduce any spatial variability, fragmented by hand and were passed through a 10 mm sieve. Finally, soil was stored at 4 °C without drying until wet physical fractionation. All analyses were performed in triplicate.

The methods used for soil fractionation were adapted from Yoder [START_REF] Yoder | A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses[END_REF] for the isolation of soil fractions located outside macroaggregates, and from Virto et al. [START_REF] Virto | Microaggregation and organic matter storage at the silt-size scale[END_REF] for the isolation of soil fractions located inside macroaggregates. Soil samples (10 g) were placed on top of a 200 µm sieve inside a tank filled with approximately 2 l of milli-Q cold water (4 °C), and were immersed into the water for 5 min before sieving. Wet sieving was an up and down movement over a total distance of 32 mm with a frequency of 30 cycles min -1 for 10 min. After wet-sieving, materials retained on the 200 µm-sieve, i.e.

water-stable macroaggregates (hereafter, macroaggregates), sand and POM were collected.

The POM fraction was isolated by flotation in water and referred to as coarse POM (cPOM: > 200 µm). Coarse sands were removed by forceps from macroaggregates; the macroaggregates were then kept for a second soil fractionation to isolate the soil fractions held inside macroaggregates (see below). The remaining suspension (< 200 µm) was sieved at 50 µm and 20 µm to obtain the 50-200 µm and 20-50 µm soil fractions, respectively.

Fine POM (fPOM: 50-200 µm) were isolated by flotation in water from the 50-200 µm soil fraction. The remaining suspension was centrifuged to obtain 0-20 µm fractions (2000 rpm for 10 min, 4 °C). These were the fractions located outside macroaggregates. To isolate the soil fractions held inside macroaggregates, water-stable macroaggregates were not dried after their isolation, but were directly immersed in 200 ml milli-Q water above a 200 µm mesh screen with fifty 6 mm glass beads [START_REF] Virto | Microaggregation and organic matter storage at the silt-size scale[END_REF]. The macroaggregates and the beads were then agitated in an end-over-end shaker for 20 min at 45 rotations min -1 .

Regular water flow through the 200 µm mesh screen ensured that the microaggregates (< 200 µm) passed through the mesh screen immediately after being released from macroaggregates, without further disruption by the beads [START_REF] Six | Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture[END_REF][START_REF] Virto | Microaggregation and organic matter storage at the silt-size scale[END_REF]. After all the macroaggregates had been broken up (20 min, determined after preliminary experiments), the water and soil were sieved as described above. The resultant organo-mineral and POM soil fractions were named: i50-200 µm, i20-50 µm, i0-20 µm, icPOM and ifPOM, where i indicate soil fractions from inside macroaggregates. The isolated fractions (organo-mineral and POM soil fractions) and unfractionated soil were either stored at -20 °C for microbial community structure analysis or oven-dried at 40 °C and ground (< 200 µm) for C and N analyses with a CHN analyser (NA 2000 N-PROTEINE) (see Supplementary material).

Nucleic acids were extracted from 0.5 g (wet weight) of unfractionated soil and each fraction described above. Bacterial 16S rRNA genes were amplified with the bacterial primers 338f-GC and 518r and the amplicons were resolved by denaturing gradient gel electrophoresis (DGGE). The full details of the DNA extraction, PCR amplification and DGGE analysis are provided in the Supplementary material. To analyse the matrix obtained from DGGE band profiles, the total band intensity was normalised for each sample (i.e. each band intensity was divided by the total band intensity of each sample). The relative abundance data from the DGGE matrix was then square root transformed and a similarity matrix from DGGE profiles was generated using the Bray-Curtis method. A dendrogram was produced from the similarity matrix using the group average linking method implemented in the software PRIMER v6 (PRIMER-E Ltd, Plymouth, UK). To test for significant differences between bacterial communities of the different soil fractions, and to correlate variation in bacterial communities to OC concentration and C:N ratio, ANOSIM and RELATE tests from PRIMER v6 software were performed, respectively (see Supplementary materials).

Macroaggregates (> 200 µm) and fractions < 50 µm constituted 75% and 20% of the soil, respectively (Table S1). Macroaggregates were mainly composed of 0-20 µm (55%) and 20-50 µm (28%) soil fractions. All POM fractions represented about 1% of the soil. The proportions of the soil fractions < 200 µm and fine POM were significantly higher inside macroaggregates than outside macroaggregates (P < 0.05, Table S1). The bacterial community structure, assessed by a fingerprinting technique (DGGE), was strongly correlated with OC concentrations (ρ = 0.73, P = 0.001), but only weakly correlated with C:N ratios (ρ = 0.32, P = 0.002). The bacterial community structure of POM fractions were strongly correlated to C:N ratios (ρ = 0.55, P = 0.004) but not to OC concentrations (ρ = 0.20, P = 0.13). The cluster analysis of the microbial structure revealed that POM communities formed separate clusters (cluster I, V and VI) from unfractionated soil and organo-mineral soil communities (cluster II, III, IV), which was confirmed by significant P values and high R values of the ANOSIM (Fig. 1; Table S2). Moreover, coarse and fine POM communities were also significantly different from each other. All of the organomineral fractions (cluster III and IV) were significantly different from the unfractionated soil (P ≤ 0.003), which all grouped together (cluster II, Fig. 1, Table S2). These results confirmed that fractioning soil can reveal specific soil bacterial communities which are hidden in unfractionated soil [START_REF] Ranjard | Heterogeneous cell density and genetic dtructure of bacterial pools associated with various Soil microenvironments as determined by enumeration and DNA fingerprinting approach (RISA)[END_REF][START_REF] Chotte | Changes in bacterial communities and Azospirillum diversity in soil fractions of a tropical soil under 3 or 19 years of natural fallow[END_REF][START_REF] Blaud | Dynamics of bacterial communities in relation to soil aggregate formation during the decomposition of 13 C-labelled rice straw[END_REF][START_REF] Davinic | Pyrosequencing and mid-infrared spectroscopy reveal distinct aggregate stratification of soil bacterial communities and organic matter composition[END_REF]. However, none of the communities associated with organo-mineral soil fractions were significantly different from each other (P > 0.05, Table S2). Finally, the dendrogram and ANOSIM analyses showed that organo-mineral soil fractions from inside and outside macroaggregates were not significantly different (P = 0.32, Fig. 1).

POM fractions (coarse and fine POM) clearly differed in the structure of their bacterial communities compared to the other soil fractions and unfractionated soil, which was mainly explained by the higher OC concentration. The specific bacterial communities on POM fractions, which accounted only for 0.3% of the soil mass (Table S1), are located on specific microhabitats which could be considered "hot spots", where biological activities are potentially extremely high relative to the surrounding matrix. Several studies have demonstrated that plant residues represent hot spots, where readily available carbon and energy resources are present. These resources influence the biomass, the activity, and the genetic structure of the soil microbial communities close to the plant residues [START_REF] Gaillard | Carbon, nitrogen and microbial gradients induced by plant residues decomposing in soil[END_REF][START_REF] Mcmahon | Dynamics of microbial communities during decomposition of carbon-13 labeled ryegrass fractions in soil[END_REF][START_REF] Nicolardot | A microcosm experiment to evaluate the influence of location and quality of plant residues on residue decomposition and genetic structure of soil microbial communities[END_REF]. However, hot spots are still too few to influence the whole soil microbial communities. Only by separating POM fractions from organo-mineral soil fractions allows access to this hidden bacterial community, as has already been shown for other soil microhabitats [START_REF] Chotte | Changes in bacterial communities and Azospirillum diversity in soil fractions of a tropical soil under 3 or 19 years of natural fallow[END_REF][START_REF] Mummey | Spatial stratification of soil bacterial populations in aggregates of diverse soils[END_REF]. Moreover, the different sizes of POM isolated in this current study harboured different bacterial communities structure. The differences in C:N ratio (which can be used as a proxy for the state of decomposition of POM) between cPOM and fPOM (~1.5 times higher in cPOM than fPOM), and the different location of coarse and fine POM, were likely to directly influence the bacterial communities. Thus, coarse and fine POM represented distinct microhabitats for the bacterial community in soil and are likely to represent two different hotspots of bacterial activity.

High levels of community structure similarities were found between organo-mineral fractions (Fig. 1, Table S2). This result is not surprising, considering that bacterial community structure was strongly correlated with chemical environment, such as OC concentration and C:N ratio, which were relatively similar among organo-mineral soil fractions in the soil studied (Table S3). However, despite the higher OC concentrations and C:N ratios, the 50-200 µm fractions did not have a different bacterial community structure from other organo-mineral soil fractions (Fig. 1, Table S2). The differences in OC resources were possibly not high enough to differentiate the bacterial community between fractions. In addition, as the OC quantity were likely distributed among soil fractions by fast macroaggregates turnover due to soil tillage [START_REF] Six | Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture[END_REF], the differentiation of specific microbial communities in such fractions could also be hindered. In a same way, as the organic concentration and quality was not different between soil fractions located inside or outside macroaggregates, bacterial community structure was not affected by location inside or outside macroaggregates. The potential fast turnover of macroaggregates due to soil tillage might also increase the turnover of microaggregates and finer fractions from inside to outside macroaggregates [START_REF] Six | Soil macroaggregate turnover and microaggregate formation: a mechanism for C sequestration under no-tillage agriculture[END_REF], reducing any potential differences between microhabitats and subsequently the bacterial community of these soil fractions.

However, the lack of differences in bacterial community structure between organo-mineral soil fractions could be also due to the low resolution of DGGE, which target only the most dominant bacterial taxa; it may be that higher resolution techniques (such as next generation sequencing) would be required to identify significant differences [START_REF] Davinic | Pyrosequencing and mid-infrared spectroscopy reveal distinct aggregate stratification of soil bacterial communities and organic matter composition[END_REF]. Nevertheless, DGGE indicated that the most dominant bacterial taxa did not differ between organo-mineral fractions located outside or inside macroaggregates.

This study clearly has shown that POM represent distinct bacterial microhabitats in soil, and that the state of decomposition of this microhabitats (i.e. coarse POM vs. fine POM) might select bacterial community, highlighting the fact that soil microhabitats are dynamic within the soil, which directly influence bacterial community. The aqueous phase containing nucleic acids was separated by centrifugation (16,000 × g) at 4 °C for 5 min. The aqueous phase was then extracted, and phenol was removed by mixing with an equal volume of chloroform-isoamyl alcohol (24:1) followed by repeated centrifugation (16,000 × g) at 4 °C for 5 min. Total nucleic acids were subsequently precipitated from the extracted aqueous layer with 2 volumes of 30% (w/vol) polyethelene glycol 6000 (Fluka BioChemika) and 1.6 M NaCl for 2 hours at room temperature, followed by centrifugation (18,000 × g) at 4 °C for 10 min. The nucleic acid pellets were finally washed in ice cold 70% (vol/vol) ethanol and air dried prior to resuspension in 20 µl of sterile milli-Q water.

PCR amplifications and Denaturing Gradient Gel Electrophoresis (DGGE) analyses

The bacterial 16S rRNA gene sequences were amplified with the bacterial primers 338f-GC [START_REF] Olsson | Ectomycorrhizal mycelia reduce bacterial activity in a sandy soil[END_REF] and 518r [START_REF] Muyzer | Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA[END_REF]. PCR amplifications were performed in 25 µl mixtures using puReTaq™ Ready-To-Go™ PCR beads (Amersham-Biosciences, Orsay, France) with 5 ng of template DNA and 1.25 μM of each primer, using a GeneAmp PCR System 9700 (Applied Biosystems, Courtaboeuf, France). Bacterial 16S rRNA genes were amplified using an initial denaturation at 94 ºC for 2 min, followed by 20 cycles of 94 ºC for 30 s (denaturing), 65 ºC for 30 s (annealing), and 72 ºC for 60 s (extension), with a 0.5 °C touchdown every cycle during the annealing stage; this was, followed by an additional 10 cycles with an annealing temperature of 55 °C, before a final extension at 72 °C for 10 min.

The amplicons were resolved by DGGE using 8% acrylamide gels (acrylamidebisacrylamide 40%, 37.5:1) (Sigma-Aldrich, St. Quentin Fallavier, France) and a gradient of 45-70% denaturant [START_REF] Muyzer | Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA[END_REF] in 1x TAE buffer with the Ingeny phorU system (Ingeny International, Goes, The Netherlands) at 60 °C and 50 mA-100 V for 17 h. Because of the number of samples, 2 DGGE gels were required for the analysis (Fig. S1). In order to compare gels and normalise gels differences, the same marker was used on both gels and PCR products of the unfractionated soil samples from four DNA extractions were used on both gels (Fig. S1). Hence, eight PCR products were obtained during the same PCR run from the unfractioned soil samples (i.e. 2 PCR per sample). Gels were stained with Ethidium Bromide and images were captured using Bio-capt software (Ets Vilbert Lourmat, France). The 16S rRNA gene DGGE band patterns were analysed using Totallab TLV120 software (Nonlinear dynamics, Newcastle, UK) to obtain matrices of band profiles with the intensity of each band.

Statistical analyses

The statistical analyses of the distribution of organo-mineral and POM soil fractions, C concentrations, and C:N ratios were performed using mean comparisons by paired student t-tests. The normality and homoscedasticity of data were checked prior to statistical analysis.

To test any significant differences between the bacterial communities of the different soil fractions or between communities from outside vs. inside macroaggregates, one-way ANOSIM (analysis of similarity: all possible permutations were done) were performed on the DGGE similarities matrix obtained using the Bray-Curtis method.

ANOSIM give the significance levels, i.e. P value, and R value, i.e. the strength of the factors on samples. R values close to 1 indicate a high separation between groups (e.g. between soil fractions), while R values close to 0 indicate a low group separation.

Correlations between the bacterial community structure and the OC concentration and C:N ratio were performed using the permutation-based test (rank correlation method: Spearman, 999 permutations) RELATE from the PRIMER software. The similarity matrix of the DGGE profiles obtained using Bray-Curtis method was correlated to the similarity matrices of OC concentration or C:N ratio obtained by Euclidean distance (Clarke and Ainsworth, 1993). Significance levels, i.e. P values, and correlation strengths, i.e. Spearman coefficient ρ were obtained. The ρ values close to 1 indicate a strong correlation, while ρ values close to 0 indicate a weak correlation. size (Table S3). All the organo-mineral soil fractions had close OC concentrations and C:N ratios with the exception of the 50-200 µm fractions (Table S3). Compared to other soil fractions, 50-200 µm fractions had significantly higher OC concentration (nearly two fold; P < 0.05) and higher C:N ratio. However, because of the weight of the 50-200 µm soil fractions (Table S2), the main soil OC reservoir was in the 0-20 µm fractions; i.e. 43% of the total OC was located in 0-20 µm fractions, 12% outside and 31% inside macroaggregates.

The OC concentration of the 20-50 µm soil fraction located inside macroaggregates significantly decreased by ~21% in comparison to outside (P < 0.05), while OC concentration of the 0-20 µm fraction inside macroaggregates slightly increased by ~5% in comparison to outside (Table S3). Location of the POM fractions inside or outside the macroaggregates did not significantly influence the OC concentration and the C:N ratio (P > 0.05). However, a decrease of ~14% in C:N ratio was observed when cPOM were located inside macroaggregates.

Supplementary references

Clarke, K.R., Ainsworth, M., 1993. A method of linking multivariate community structure to environmental variables. Mar. Ecol. Prog. Ser. 92, 205-205. Griffiths, R.I., Whiteley, A.S., O'Donnell, A.G., Bailey, M.J., 2000. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA-and rRNA-based microbial community composition. Appl. Environ.

Microbiol. 66, 5488-5491. Table S1. and coarse and fine particulate organic matter (POM) soil fractions located outside and inside macroaggregates (> 200 μm) (mean ± standard deviation, n = 3). cPOM: coarse particulate organic matter (> 200 μm). fPOM: fine particulate organic matter (< 200 μm).

Fractions outside macroaggregates

Fractions inside macroaggregates Fractions (µm) (g 100 g -1 soil) (g 100 g -1 soil) (g 100 g -1 macroaggregates) Values followed by different letters for the same columns are significantly different (P < 0.05). * indicates significant differences between fractions outside and inside macroaggregates (* P < 0.05).

Figure 1 .

 1 Figure 1. Dendrogram of DGGE profiles of bacterial 16S rRNA genes amplified from

Figure S1 :

 S1 Figure S1: DGGE gels of bacterial 16S rRNA genes amplified from unfractionated soil

Table S2 :

 S2 One-way ANOSIM showing variability in the structure of bacteria between organo-mineral (0-20, 20-50 and 50-200 μm), coarse and fine particulate organic matter (POM) fractions and unfractionated soil. ANOSIM R values and P values are given.

	Significant values at P < 0.05 are shown in bold text. UF soil: unfractionated soil. cPOM:
	coarse particulate organic matter (> 200 μm). fPOM: fine particulate organic matter (< 200
	μm).
	Factors compared	R value	P value
	cPOM vs. fPOM	0.42	0.029
	cPOM vs. 50-200	0.74	0.005
	cPOM vs. 20-50	0.73	0.005
	cPOM vs. 0-20	0.80	0.005
	fPOM vs. 50-200	0.52	0.014
	fPOM vs. 20-50	0.52	0.01
	fPOM vs. 0-20	0.78	0.005
	50-200 vs. 20-50	-0.05	0.522
	50-200 vs. 0-20	0.28	0.063
	0-20 vs. 20-50	0.12	0.123
	UF Soil vs. cPOM	0.87	0.002
	UF Soil vs. fPOM	0.88	0.002
	UF Soil vs. 50-200	0.62	0.0003
	UF Soil vs. 20-50	0.55	0.0003
	UF Soil vs. 0-20	0.41	0.003

Table S3 .

 S3  and coarse and fine particulate organic matter (POM) soil fractions located outside and inside macroaggregates (> 200 μm) (mean ± standard deviation, n = 3). UF soil: unfractionated soil. cPOM: coarse particulate organic matter (> 200 μm). fPOM: fine particulate organic matter (< 200 μm).

		Fractions outside macroaggregates	Fractions inside macroaggregates
		C	C		C	C
	Fractions
		g C kg -1	g C 100g -1 C	C:N	g C kg -1	g C 100g -1 C	C:N
	(µm)
		fractions	soil		fractions	soil
	UF soil	26.8 ± 0.2		12.4 ± 0.8
	> 200	23.9 ± 1.3 A	66.6 ± 6.9	10.7 ± 0.7 A
	50-200	38.2 ± 6.9 C	2.3 ± 0.9	15.0 ± 0.9 C	42.6 ± 1.4 B	9.8 ± 0.9	15.9 ± 0.5 A
	20-50	20.5 ± 1.4 E	3.8 ± 1.2	11.2 ± 1.0 A	16.1 ± 0.5 * D	10.8 ± 0.1	10.5 ± 1.3 BC
	0-20	22.4 ± 0.5 A	11.8 ± 1.4	8.8 ± 0.7 D	23.5 ± 0.3 * E	31.1 ± 0.6	9.2 ± 0.5 C
	cPOM	257.9 ± 30.3 B	1.2 ± 0.4	19.5 ± 1.2 B 303.1 ± 14.2 A	2.1 ± 0.3	16.8 ± 1.3 A
	fPOM	147.8 ± 4.4 D	1.2 ± 0.6	12.3 ± 0.9 A	125.6 ± 13.6 C	3.4 ± 0.6	11.2 ± 0.8 B
	Total		= 86.9 ± 4.8			= 57.2 ± 0.6

Acknowledgement

We are grateful for the assistance and access to the soil samples provided by Fabrice Martin (INRA, Epoisse). We would also like to thank Joele Toucet for her help in laboratory work, Dr. Eric Blanchart for his useful advice on the article, and Dr Susan Johnston for proofreading the article.

Supplementary material and methods

C and N analysis

Unfractionated-soil and organo-mineral soil fraction samples were decarbonated prior to C and N determination. Ten ml of water was added to 1 g of soil or soil fraction and 0.5 M HCl was then dripped onto the sample until there was no more effervescence.

Samples were then centrifuged for 5 min (270 x g), washed in water and centrifuged again until soil pH reached 7. All samples were then oven-dried for CHN analyses using a CHN analyser (NA 2000 N-PROTEINE) [START_REF] Pansu | Organic and total C, N (H, O. S) analysis[END_REF].

DNA extraction

DNA was extracted from 0.5 g (wet weight) of unfractionated soil and each soil fraction studied. For the coarse and fine particulate organic matter (POM) samples, 2 replicates for each size of POM were pooled, as only a very small amount of POM was recovered in some individual replicates (< 0.3 g). In each fraction, the replicates with the smallest amount of POM were pooled, so that, for each POM fraction, DNA extractions were made of two samples: one composite POM sample and one single fractionation replicate. DNA extraction followed the protocol described by Griffiths et al. (2000). Briefly, 0.5 g glass beads (0.1 mm in diameter), 1 ml lysis buffer, 0.5 ml of hexadecyltrimethylammonium bromide (CTAB), and 0.5 ml phenol chloroform isoamyl alcohol (25:24:1) (pH 8.0) were added to each organo-mineral or POM soil fraction. The bacterial cells in organo-mineral and POM soil fractions were then lysed by bead beating twice for 30 s (Retsch MM200).

Supplementary results

Soil and POM fractions size distribution

The soil fractionation procedure resulted in very small losses of material, as the mean mass recovery was about 96% of the original unfractionated soil and about 94% of macroaggregates (Table S1).

Organic carbon (OC) concentrations and C:N ratios of the organo-mineral and POM soil fractions

The soil fractionation procedure resulted in slight losses of OC, as the mean carbon concentration recovery was 87% of the original unfractionated soil and 86% of the macroaggregates (Table S3). POM fractions had higher OC concentration and C:N ratio than other soil fractions. The OC concentrations and C:N ratios decreased with the POM


Caroline Vayne

Jérôme Rollin

Yves Gruel

Claire Pouplard

Hubert Galinat

Olivier Huet

Vincent Mémier

Thomas Geeraerts

Raphael Marlu

Gilles Pernod

PF4 Immunoassays in Vaccine-Induced Thrombotic Thrombocytopenia

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PF4 Immunoassays in

Lille University Hospital, Lille, France

To the Editor: In a recent study, Greinacher et al. [START_REF] Greinacher | Thrombotic thrombocytopenia after ChAdOx1 nCov-19 vaccination[END_REF] reported thrombotic complications, mostly cerebral vein thrombosis, associated with thrombocytopenia in 11 patients after they had been vaccinated with ChAdOx1 nCoV-19 (AstraZeneca). Although none of these patients had received heparin, the authors detected high titers of anti-platelet factor 4 (PF4)-heparin antibodies that strongly activated platelets in vitro without heparin and in the presence of PF4. This syndrome, which resembles autoimmune heparin-induced thrombocytopenia, was called vaccine-induced immune thrombotic thrombocytopenia (VITT), and an algorithm for the management of this syndrome was proposed on the basis of immunoassays detecting anti-PF4-heparin antibodies.

Between March 19 and April 1, 2021, plasma samples from nine patients (median age, 44 years) with suspected VITT after vaccination with ChAdOx1 nCoV-19 were analyzed in our laboratory (Table S1 in the Supplementary Appendix, available with the full text of this letter at NEJM.org). Cerebral vein thrombosis (in six patients) and splanchnic vein thrombosis (in five patients) were the most common events. All the patients had severe thrombocytopenia (median platelet count nadir, 29,000 per cubic millimeter; range, 9 to 61,000) except for one woman with both cerebral vein thrombosis and splanchnic vein thrombosis. Two rapid immunoassays widely used for the diagnosis of heparin-induced thrombocytopenia (STic Expert HIT and HemosIL AcuStar HIT-IgG) were performed on plasma samples to detect PF4-specific antibodies, and the results were negative in all the patients. Two other rapid tests had been performed in some patients by the referring laboratories and were negative, except in one patient, who had an equivocal result (Figure 1A). Panel C shows that a serotonin release assay (SRA) should be performed with the use of PF4 to detect platelet-activating antibodies to VITT. SRA was performed by incubating 75 μl of washed platelets obtained from healthy persons with 20 μl of plasma obtained from each patient, either in the absence (standard SRA) or the presence (PF4-SRA) of 10 μg per milliliter of PF4. All tests were performed with or without unfractionated heparin at 0.1 IU per milliliter (UFH 0.1), 0.5 IU per milliliter (UFH 0.5), and 10 IU per milliliter (UFH 10).

Clinically significant and strong platelet activation, with maximum release ranging from 36 to 99%, was measured in seven of the nine patients only when PF4 was present in the reaction mixture. Moreover, platelet activation was not inhibited by therapeutic concentrations of UFH 0.1 or UFH 0.5. In contrast, platelet activation was completely abolished by 10 μg per milliliter of IV.3, a monoclonal antibody specific for FcγRIIA, or 6 U of IdeS, an IgGdegrading enzyme of Streptococcus pyogenes, preincubated for 15 minutes at 37°C in the plasma sample of each patient tested (five patients) before SRA.

We also tested all plasma samples with three different PF4-specific enzyme-linked immunosorbent assays and obtained variable results (Figure 1B). Significant levels of IgG antibodies to PF4 were detected in seven patients only by the assay that used PF4-poly(vinyl sulfonate) (PVS) complex as the antigenic target. In addition, optical density values were variable and lower than those previously reported with a similar test. [START_REF] Schultz | Thrombosis and thrombocytopenia after ChAdOx1 nCoV-19 vaccination[END_REF] The diagnosis of VITT was confirmed by PF4-serotonin release assay 3 in all seven patients with IgG antibodies to PF4-PVS (Figure 1C), whereas a standard serotonin release assay was negative in two patients. Platelet activation was suppressed by IV.3, a monoclonal antibody that binds FcγRIIA receptors, but also by IdeS (IgG-degrading enzyme derived from Streptococcus pyogenes) (Figure 1C), a protease that also inactivates heparin-induced thrombocytopenia IgG antibodies. [START_REF] Kizlik-Masson | Cleavage of anti-PF4/heparin IgG by a bacterial protease and potential benefit in heparin-induced thrombocytopenia[END_REF] Intravenous immune globulins may be inappropriate for severe cerebral vein thrombosis with intracranial hypertension. IdeS (imlifidase) may be an effective treatment and needs to be evaluated.

Our results provide further support to show that rapid immunoassays should be avoided in the detection of PF4-specific antibodies in patients with suspected VITT. Therefore, the use of a sensitive, quantitative, immunologic test is strongly recommended, because according to the recently proposed algorithm, [START_REF] Greinacher | Thrombotic thrombocytopenia after ChAdOx1 nCov-19 vaccination[END_REF][START_REF] Oldenburg | Diagnosis and management of vaccine-related thrombosis following AstraZeneca COVID-19 vaccination: guidance statement from the GTH[END_REF] nonheparin anticoagulants should be preferred when clinically significant levels of anti-PF4 antibodies are detected.

  Vaccine-Induced Thrombotic Thrombocytopenia Caroline Vayne, Pharm.D., Jérôme Rollin, Ph.D., Yves Gruel, M.D., and Claire Pouplard, Pharm.D. Tours University Hospital, Tours, France, Hubert Galinat, Pharm.D. and Olivier Huet, M.D. Brest University Hospital, Brest, France Vincent Mémier, M.D. and Thomas Geeraerts, M.D. Toulouse University Hospital, Toulouse, France Raphael Marlu, M.D. and Gilles Pernod, M.D. Grenoble Alpes University, Grenoble, France Guillaume Mourey, M.D. Etablissement Français du Sang, Besançon, France Alexandra Fournel, M.D. Besançon University Hospital, Besançon, France Charlotte Cordonnier, M.D. and Sophie Susen, M.D.

Figure 1 PF4-

 1 Figure 1 PF4-Specific Immunoassays and SRA in Patients with Suspected VITT. Panel A shows that rapid immunoassays do not detect antibodies to platelet factor 4 (PF4) associated with vaccine-induced immune thrombotic thrombocytopenia (VITT). All the plasma samples from the patients with findings suggestive of VITT were tested with two rapid immunoassays (STic Expert HIT, Stago, and HemosIL AcuStar HIT-IgG, Werfen), and negative results were obtained in every case. Two other rapid assays had also been performed in some patients (each represented by a specific symbol) in the referring centers (ID-PaGIA H/PF4, DiaMed, and HemosIL HIT-Ab, Werfen), and they also showed negative or doubtful (in patient *, who had an initial positive result but then tested negative) results. Panel B shows that the sensitivity of enzyme-linked immunosorbent assays (ELISAs) to detect PF4-specific IgG antibodies depends on the antigen target. Levels of PF4-specific IgG antibodies were evaluated in plasma samples from the nine patients with suspected VITT (each represented by a specific symbol) with the use of three different ELISAs with varying antigen targets (PF4-heparin complexes [Asserachrom HPIA, Stago], PF4 released from a platelet lysate [PL] and complexed with heparin [Zymutest HIA IgG, Hyphen], and PF4poly[vinyl sulfonate] [PVS] complexes [Lifecodes PF4 IgG, Immucor]). OD denotes optical density.