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Supplementary Information for: In situ relationships between microbiota and potential pathobiota in Arabidopsis thaliana Claudia Bartoli1¶, Léa Frachon1¶, Matthieu Barret2, Mylène Rigal1, Carine Huard-Chauveau1, Baptiste Mayjonade1, Catherine Zanchetta3, Olivier Bouchez3, Dominique Roby1, Sébastien 1 1* Carrère , Fabrice Roux Affiliations: 1 LIPM, Université de Toulouse, INRA, CNRS, Castanet-Tolosan, France 2 IRHS, INRA, AGROCAMPUS-Ouest, Université d’Angers, SFR4207 QUASAV, 42, rue Georges Morel, 49071 Beaucouzé, France 3 INRA, GeT-PlaGe, Genotoul, Castanet-Tolosan, France ¶ These authors contributed equally to this work. *To whom correspondence should be addressed. E-mail: [email protected] This file includes: Supplementary Text: Material and Methods, Results and References Supplementary Figures 1-17 Supplementary Tables 1-14 1 SUPPLEMENTARY TEXT MATERIAL AND METHODS Plant material In agreement with previous observations in natural populations of A. thaliana located in northeast of Spain (Montesinos et al., 2009), the 163 populations studied here strongly differed in their main germination flush in autumn 2014, thereby leading to the observation of different life plant stages among populations. We therefore defined three seasonal groups. The first group, hereafter named ‘autumn’, corresponded to 84 populations where most plants had reached the 5-leaf rosette stage during the 23-day sampling period in autumn (mid – November 2014 – early December 2014). The second group, hereafter named ‘spring with autumn’, corresponded to 80 populations already sampled in autumn and additionally sampled during a 29-day period in early-spring (mid-February 2015 – mid-March 2015). Four populations sampled during autumn were not collected in early-spring to avoid modifications of their demographic dynamics. The third group, hereafter named ‘spring without autumn’, corresponded to 79 populations only sampled during the 29-day period in early-spring. These populations were not sampled in autumn because the life stage of most plants was between 2- cotyledon and 4-leaf. The ‘autumn’ and ‘spring with autumn’ groups allowed to test whether the dynamics of diversity and composition of the bacterial communities across seasons was dependent on the population considered. On the other hand, the ‘spring with autumn’ and ‘spring without autumn’ groups allowed to test whether the diversity and composition of bacterial communities in spring 2015 were affected by germination timing in autumn 2014. 2 Validation of the gyrB marker used for characterization of bacterial communities To characterize the A. thaliana bacterial fraction, a portion of gyrB (encoding the β subunit of the bacterial gyrase) has been used. This prokaryote-specific molecular marker has a deeper taxonomic resolution (species-level) than other molecular markers designed on the hypervariable regions of the 16S rRNA gene (Barret et al., 2015). Furthermore, this single- copy gene limits the overestimation of taxa carrying multiple copies of rrn operons. The gyrB prevalence was investigated in 32,062 bacterial genomic sequences available in the IMG database v4 (Markowitz et al., 2014) at the time of analysis (10th December 2015). Coding sequences that exclusively belong to the protein family TIGR01059 and KO2470 were defined as GyrB orthologs and retrieved for further analysis (30,627 hits found in 30,175 genomic sequences). The corresponding nucleotide sequences were aligned against a reference gyrB alignment (Barret et al., 2015) with the align.seqs function in mothur (Schloss et al., 2009). Sequences that did not align (102) were discarded and only unique sequences were conserved in the reference alignment (10,427 haplotypes). According to the gANI (Varghese et al., 2015), the gyrB marker was highly precise (0.964) and sensitive (0.955) at a genetic distance of 0.02 (98% identity cutoff). In order to assess the potential amplification bias of gyrB, we amplified a mock community containing 52 bacterial strains (Supplementary Data 1) with both gyrB and 16S rRNA V4 region primers (Caporaso et al., 2011). Results showed that 16S rRNA gene and gyrB sequences were clustered with an identity threshold of 97% and 98%, respectively. Based on this clustering, we obtained n = 19 OTUs with the 16 rRNA gene and n = 45 OTUs with gyrB. The 52 members of the mock community were all detected with the 16S rRNA gene marker, while three strains were not detected with the gyrB segment (Supplementary Data 1). Overall, our results suggested a better taxonomic resolution of the gyrB but associated with a small cost on bacterial detection. 3 Sampling, generation of the gyrB amplicons and sequences Plants were excavated using flame-sterilized spoons and then manipulated with flame- sterilized forceps on a sterilized porcelain plate. Gloves and plate were sterilized by using Surface'SafeAnios®. Roots and rosettes were rinsed into individual tubes of sterilized distilled water to remove all visible rhizosphere. Both leaves and roots were then placed into sterilized tubes and immediately stored in dry ice. Samples were stored at -80°C prior DNA extraction. For each plant, we recorded the sampling date. In addition, the age of each plant was approximated by measuring the maximum rosette diameter and by counting the number of leaves. Finally, each plant was visualized with the human eye for the presence of disease symptoms on the rosette leaves. Following Roux et al. (2010), the presence of disease symptoms was determined by the presence of chlorosis, water soaking, or cell death. All plants were therefore classified according to a binary category, i.e. presence or absence of visible disease symptoms on the rosette leaves. The DNA of each leaf and root sample was extracted as follows: i) leaves were placed in 96 well plates containing sterilized beads and homogenized for 1 min with 30 vibrations per second in a plate shaker and incubated 30 min in 500 µl of buffer containing 200 mM of Tris-HCl at pH 7.5, 250 mM of NaCl, 25 mM of EDTA and 0.5% SDS, ii) roots were placed in Eppendorf tubes and incubated 10 min in a sonicator bath and treated with the same conditions described above for the leaves, iii) for both leaves and roots phenol/chloroform 25:24:1 pH 8.0 (Sigma Aldrich®) was used for extraction and purification of the DNA, and iv) DNA was precipitated with isopropanol and washed with 70% EtOH and eluted in 100 µl of DNA-free water. DNA samples were stored at -20°C prior PCR amplification. Briefly, three tags were added at each 5' and 3' of the original primers to allow the multiplexing of three plates. Primers including Illumina adapter sequences and without 4 internal tags were: Fw (5'- CTTTCCCTACACGACGCTCTTCCGATCTMGNCCNGSNATGTAYATHGG - 3') and Rv (5'- GGAGTTCAGACGTGTGCTCTTCCGATCTCCTCTTACNCCRTGNARDCCDCCNGA - 3'). The internal tags for multiplexing consisted in: TAG1 (Fw - GACTAC, Rv - AAGGCC), TAG2 (Fw - CTGGTT, Rv - GTCAGG), TAG3 (Fw - ACTCGA, Rv – CCTCTT). MTP taq DNA Polymerase-Sigma Aldrich® was used and the PCR mix was composed by 2.5 µl of Taq Buffer, 0.2 µl of dNTPs 10mM, 1 µl of Fw Primers (10p/mol), 1 µl of Rv Primers (10p/mol), 0.3 µl of Taq polymerase, and 1 µl of 10 fold diluted DNA for a final volume of 25 µl. PCR amplifications were performed by using 95°C for 5 min of initial denaturation followed by 40 cycles with 95°C for 30 sec, 52°C for 1 min and 30 sec, 68°C for 1 min and a final elongation of 68°C for 5 min. Negative controls were also added to investigate whether amplification of bacterial DNA was detected on (i) the water used for both leaves and roots washing, (ii) the water used for DNA elution and (iii) the water used for PCR mix. Statistical analyses Natural variation of microbiota and potential pathobiota Natural variation for the eight descriptors of microbiota (i.e. richness, α-diversity Shannon index, PCo1 and PCo2) and potential pathobiota microbiota (i.e. richness, α-diversity Shannon index, PCo1 and PCo2) was explored using the following mixed models: 1. To explore natural variation in populations collected both in autumn and spring, we used the following mixed model: Yijklmno = µtrait + seasoni+ compartmentj + seasoni× compartmentj + populationk + seasoni× populationk + compartmentj× populationk + seasoni× compartmentj× populationk+ sampling_datel(seasoni) + diameterm(seasoni) + leaf_numbern(seasoni) + obso+ εijklmno (1) 5 where ‘Y’ is one of the 7 descriptors,‘µ’ is the overall phenotypic mean; ‘season’ accounts for differences between autumn and spring; ‘compartment’ accounts for differences between leaves and roots; ‘population’ measures the effect of populations; interaction terms involving the ‘population’ term account for variation among populations in reaction norms across the two seasons and/or the two plant compartments; ‘ε’ is the residual term. Four terms were added to control for noise that may affect significance of the other model terms. First, ‘sampling_date’ accounts for the number of days since the first population was collected within each season. Second, because age can shape leaf and root microbiota (Wagner et al., 2016), the two traits ‘rosette diameter’ and ‘leaf number’ were used as proxies of plant age. Third, ‘obs’ corresponds to the total number of observations and accounts for technical noise attributable to sequencing depth. 2. To explore natural variation in populations collected in spring, we used the following mixed model: Yijklmno = µtrait + w/wo_Autumni+ compartmentj + w/wo_Autumni× compartmentj + populationk(w/wo_Autumni) + compartmentj×