Paul J Mcmurdie

Susan Holmes

Simon Jarman

Microbial communities Anouk Zancarini -Tools in molecular data analysis

Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition

Learning objectives

What is microbiome?

Ø W h o i s t h e r e ? W h a t a r e t h e y d o i n g ? Ø D i f f e r e n t a p p r o a c h e s b a s e d o n D N A

• M e t a g e n o m i c s = a l l D N A

• M e t a b a r c o d i n g = o n e s p e c i f i c u b i q u i t o u s g e n e w i t h c o n s e r v e d a n d v a r i a b l e r e g i o n s ( 1 6 S r R N A , 1 8 S r R N A o r I T S )

Main biological questions and methods

Anouk Zancarini -Tools in molecular data analysis -March 2021

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition Defining the core Arabidopsis thaliana root microbiome Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11 Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation [1][2][3] . Colonization of the root occurs despite a sophisticated plant immune system 4,5 , suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and chimaeric amplicons. We developed a robust experimental system to sample repeatedly the root microbiome using high-throughput sequencing. Our results confirm many of the general conclusions from earlier studies and, because of controlled experimental design and the power of deep sequencing, provide a key step towards the definition of this microbiome's functional capacity and the host genes that potentially contribute to microbial association phenotypes. Such plant genes would constitute major agronomic targets.

Learning objectives

Requirements

We used 454 pyrosequencing to sequence 16S ribosomal RNA (rRNA) gene amplicons for DNA prepared from eight diverse, inbred A. thaliana accessions. Plants were grown from surface-sterile seeds in climate-controlled conditions in two diverse soils, respectively termed

Process overview

Step 1: From sample to sequences Sampling Sterilized rice seeds were germinated and grown under controlled greenhouse conditions in soil collected from three rice fields across the Central Valley of California (SI Appendix, Fig. S1). We analyzed the bacterial and archaeal microbiomes from three separate rhizocompartments: the rhizosphere, rhizoplane, and endosphere (Fig. 1A). Because the root microbiome has been shown to correlate with the developmental stage of the plant (10), the root-associated microbial communities were sampled at 42 d (6 wk), when rice plants from all genotypes were well-established in the soil but still in their vegetative phase of growth. For our study, the rhizosphere compartment was com- Bulk soil Rhizosphere § Three compartments q Bulk soil q Rhizosphere soil q Endosphere LETTER doi:10.1038/nature11237

Defining the core Arabidopsis thaliana root microbiome Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11 Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation 1-3 . Colonization of the root occurs despite a sophisticated plant immune system 4,5 , suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We describe different bacterial communities in two geochemically distinct bulk soils and in rhizosphere and endophytic compartments prepared from roots grown in these soils. The communities in each compartment are strongly influenced by soil type. Endophytic compartments from both soils feature overlapping, low-complexity communities that are markedly enriched in Actinobacteria and specific families from other phyla, notably Proteobacteria. Some bacteria vary quantitatively between plants of different developmental stage and genotype. Our rigorous definition of an endophytic compartment microbiome should facilitate controlled dissection of plantmicrobe interactions derived from complex soil communities.

Roots influence the rhizosphere by altering soil pH, soil structure, oxygen availability, antimicrobial concentration, and quorum-sensing mimicry, and by providing an energy source of dead root material and carbon-rich exudates 6,7 . The microbiota inhabiting this niche can both benefit and undermine plant health; shifting this balance is of agronomic interest. Mutualistic microbes may provide the plant with physiologically accessible nutrients and phytohormones that improve plant growth, may suppress phytopathogens or may help plants withstand heat, salt and drought 8,9 . The rhizosphere community is a subset of soil microbes that are subsequently filtered via niche utilization attributes and interactions with the host to inhabit the endophytic compartment 10 (EC). Although a variety of microbes may enter and become transient endophytes, those consistently found inside roots are candidate symbionts or stealthy pathogens 10,11 . Notably, Arabidopsis and other Brassicaceae are not well colonized by arbuscular mycorrhizal fungi, implying that other microorganisms may fill this niche.

Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and chimaeric amplicons. We developed a robust experimental system to sample repeatedly the root microbiome using high-throughput sequencing. Our results confirm many of the general conclusions from earlier studies and, because of controlled experimental design and the power of deep sequencing, provide a key step towards the definition of this microbiome's functional capacity and the host genes that potentially contribute to microbial association phenotypes. Such plant genes would constitute major agronomic targets.

We used 454 pyrosequencing to sequence 16S ribosomal RNA (rRNA) gene amplicons for DNA prepared from eight diverse, inbred A. thaliana accessions. Plants were grown from surface-sterile seeds in climate-controlled conditions in two diverse soils, respectively termed Mason Farm and Clayton (Supplementary Table 1; detailed in Supplementary Information). For each soil, we assayed multiple individuals from each A. thaliana accession grown from sterile seeds in both soils across independent full-factorial biological replicates, in which all genotypes and bulk soils (pots without a plant) for a given soil type were grown in parallel (Supplementary Table 2). We isolated separate rhizosphere and EC fractions from individual plant root systems (Supplementary Fig. 1 and Supplementary Table 2). We established 1114F and 1392R as our primer pair (Supplementary Information and Supplementary Fig. 2). Using an otupipe-based pipeline (http://drive5.com/otupipe/), we grouped sequences into 97%-identical operational taxonomic units (OTUs), reduced noise and removed chimaeras. We determined technical reproducibility thresholds to conclude that OTUs defined by $25 reads in $5 samples (hereafter 25 3 5) are individually 'measurable OTUs' 16,17 (Supplementary Figs 2 and 10). All data reported here are from one run of our otupipe-based pipeline (Supplementary Fig. 3 and Supplementary Database 1).

Excluding additional control samples, we ribotyped 1,248 samples comprising 111 bulk soil, 613 rhizosphere and 524 EC samples, generating 9,787,070 high-quality reads (Supplementary Figs 3 and4a-c). After removing plant-sequence-derived OTUs, we obtained a table of usable OTU read counts per sample containing 6,387,407 reads distributed across 18,783 OTUs. We normalized this table of usable reads by rarefying to 1,000 reads per sample (Supplementary Database 2a) or, alternatively, by dividing the reads per OTU in a sample by the sum of usable reads in that sample, resulting in a table

Process overview

Step 1: From sample to sequences Sampling Sterilized rice seeds were germinated and grown under controlled greenhouse conditions in soil collected from three rice fields across the Central Valley of California (SI Appendix, Fig. S1). We analyzed the bacterial and archaeal microbiomes from three separate rhizocompartments: the rhizosphere, rhizoplane, and endosphere (Fig. 1A). Because the root microbiome has been shown to correlate with the developmental stage of the plant (10), the root-associated microbial communities were sampled at 42 d (6 wk), when rice plants from all genotypes were well-established in the soil but still in their vegetative phase of growth. For our study, the rhizosphere compartment was com- Defining the core Arabidopsis thaliana root microbiome Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11 Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation 1-3 . Colonization of the root occurs despite a sophisticated plant immune system 4,5 , suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We describe different bacterial communities in two geochemically distinct bulk soils and in rhizosphere and endophytic compartments prepared from roots grown in these soils. The communities in each compartment are strongly influenced by soil type. Endophytic compartments from both soils feature overlapping, low-complexity communities that are markedly enriched in Actinobacteria and specific families from other phyla, notably Proteobacteria. Some bacteria vary quantitatively between plants of different developmental stage and genotype. Our rigorous definition of an endophytic compartment microbiome should facilitate controlled dissection of plantmicrobe interactions derived from complex soil communities.

Roots influence the rhizosphere by altering soil pH, soil structure, oxygen availability, antimicrobial concentration, and quorum-sensing mimicry, and by providing an energy source of dead root material and carbon-rich exudates 6,7 . The microbiota inhabiting this niche can both benefit and undermine plant health; shifting this balance is of agronomic interest. Mutualistic microbes may provide the plant with physiologically accessible nutrients and phytohormones that improve plant growth, may suppress phytopathogens or may help plants withstand heat, salt and drought 8,9 . The rhizosphere community is a subset of soil microbes that are subsequently filtered via niche utilization attributes and interactions with the host to inhabit the endophytic compartment 10 (EC). Although a variety of microbes may enter and become transient endophytes, those consistently found inside roots are candidate symbionts or stealthy pathogens 10,11 . Notably, Arabidopsis and other Brassicaceae are not well colonized by arbuscular mycorrhizal fungi, implying that other microorganisms may fill this niche.

Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and chimaeric amplicons. We developed a robust experimental system to sample repeatedly the root microbiome using high-throughput sequencing. Our results confirm many of the general conclusions from earlier studies and, because of controlled experimental design and the power of deep sequencing, provide a key step towards the definition of this microbiome's functional capacity and the host genes that potentially contribute to microbial association phenotypes. Such plant genes would constitute major agronomic targets.

We used 454 pyrosequencing to sequence 16S ribosomal RNA (rRNA) gene amplicons for DNA prepared from eight diverse, inbred A. thaliana accessions. Plants were grown from surface-sterile seeds in climate-controlled conditions in two diverse soils, respectively termed Mason Farm and Clayton (Supplementary Table 1; detailed in Supplementary Information). For each soil, we assayed multiple individuals from each A. thaliana accession grown from sterile seeds in both soils across independent full-factorial biological replicates, in which all genotypes and bulk soils (pots without a plant) for a given soil type were grown in parallel (Supplementary Table 2). We isolated separate rhizosphere and EC fractions from individual plant root systems (Supplementary Fig. 1 and Supplementary Table 2). We established 1114F and 1392R as our primer pair (Supplementary Information and Supplementary Fig. 2). Using an otupipe-based pipeline (http://drive5.com/otupipe/), we grouped sequences into 97%-identical operational taxonomic units (OTUs), reduced noise and removed chimaeras. We determined technical reproducibility thresholds to conclude that OTUs defined by $25 reads in $5 samples (hereafter 25 3 5) are individually 'measurable OTUs' 16,17 (Supplementary Figs 2 and 10). All data reported here are from one run of our otupipe-based pipeline (Supplementary Fig. 3 and Supplementary Database 1).

Excluding additional control samples, we ribotyped 1,248 samples comprising 111 bulk soil, 613 rhizosphere and 524 EC samples, generating 9,787,070 high-quality reads (Supplementary Figs 3 and4a-c). After removing plant-sequence-derived OTUs, we obtained a table of usable OTU read counts per sample containing 6,387,407 reads distributed across 18,783 OTUs. We normalized this table of usable reads by rarefying to 1,000 reads per sample (Supplementary Database 2a) or, alternatively, by dividing the reads per OTU in a sample by the sum of usable reads in that sample, resulting in a table Ø D o n 't f o r g e t t h a t t h e r e a r e b i a s I t w i l l b e d i f f i c u l t t o

Process overview

Amplification

• A s s e s s t h e e n t i r e m i c r o b i a l c o m m u n i t y

• O b t a i n s a m e a m o u n t o f s e q u e n c e s p e r s a m p l e

Step 1: From sample to sequences

Anouk Zancarini -Tools in molecular data analysis -March 2021

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition

Learning objectives

Process overview

Step 2: From sequences to microbiota data sets § De-multiplex (i. Defining the core Arabidopsis thaliana root microbiome

Process overview

We previously introdu Algorithm (DADA), a amplicon errors without fine-scale variation in 45 putting few false positive

Here we present DAD github.com/benjjneb/dada and improves the DADA a ity-aware model of Illumin inferred by dividing ampl the error model (Online applicable to any genetic l the full amplicon workflo ence, chimera identificati

We compared DADA2 UPARSE, an OTU-constr We present the open-source software package DADA2 for modeling and correcting Illumina-sequenced amplicon errors (https://github.com/benjjneb/dada2). DADA2 infers sample sequences exactly and resolves differences of as little as 1 nucleotide. In several mock communities, DADA2 identified

DADA2: High-resolution sample inference from Illumina amplicon data

BRIEF COMMUNICATIONS

We previously introduced the Divisive Amplicon Denoising Algorithm (DADA), a model-based approach for correcting amplicon errors without constructing OTUs 5 . DADA identified fine-scale variation in 454-sequenced amplicon data while outputting few false positives 2,5 .

Here we present DADA2, an open-source R package (https:// github.com/benjjneb/dada2, Supplementary Software) that extends and improves the DADA algorithm.

Process overview

35

Step 2: From sequences to microbiota data sets Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation 1-3 . Colonization of the root occurs despite a sophisticated plant immune system 4,5 , suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and chimaeric amplicons. We developed a robust experimental system to sample repeatedly the root microbiome using high-throughput sequencing. Our results confirm many of the general conclusions from earlier studies and, because of controlled experimental design and the power of deep sequencing, provide a key step towards the definition of this microbiome's functional capacity and the host genes that poten-

Taxonomic assignment

Strain -Ø D a t a p r e -p r o c e s s i n g : a l w a y s a t r a d e -o f f b e t w e e n q u a l i t y a n d q u a n t i t y Ø O T U O p e r a t i o n a l T a x o n o m i c U n i t s ≠ A S V A m p l i c o n S e q u e n c e V a r i a n t s Ø G o f r o m f a s t a f i l e s t o t h r e e t a b l e s • o c c u r r e n c e t a b l e • t a x o n o m i c a s s i g n a t i o n • s a m p l e m e t a d a t a

Step 2: From sequences to microbiota data sets

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition

Learning objectives

Part 1

• Definitions

• Microbiome importance • Scientific questions

• Differences between metagenomics and metabarcoding

What is microbiome? Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation 1-3 . Colonization of the root occurs despite a sophisticated plant immune system 4,5 , suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We describe different bacterial communities in two geochemically distinct bulk soils and in rhizosphere and endophytic compartments prepared from roots grown in these soils. The communities in each compartment are strongly influenced by soil type. Endophytic compartments from both soils feature overlapping, low-complexity communities that are markedly enriched in Actinobacteria and specific families from other phyla, notably Proteobacteria. Some bacteria vary quantitatively between plants of different developmental stage and genotype. Our rigorous definition of an endophytic compartment microbiome should facilitate controlled dissection of plantmicrobe interactions derived from complex soil communities.

Roots influence the rhizosphere by altering soil pH, soil structure, oxygen availability, antimicrobial concentration, and quorum-sensing mimicry, and by providing an energy source of dead root material and carbon-rich exudates 6,7 . The microbiota inhabiting this niche can both benefit and undermine plant health; shifting this balance is of agronomic interest. Mutualistic microbes may provide the plant with physiologically accessible nutrients and phytohormones that improve plant growth, may suppress phytopathogens or may help plants withstand heat, salt and drought 8,9 . The rhizosphere community is a subset of soil microbes that are subsequently filtered via niche utilization attributes and interactions with the host to inhabit the endophytic compartment 10 (EC). Although a variety of microbes may enter and become transient endophytes, those consistently found inside roots are Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and chimaeric amplicons. We developed a robust experimental system to sample repeatedly the root microbiome using high-throughput sequencing. Our results confirm many of the general conclusions from earlier studies and, because of controlled experimental design and the power of deep sequencing, provide a key step towards the definition of this microbiome's functional capacity and the host genes that potentially contribute to microbial association phenotypes. Such plant genes would constitute major agronomic targets.

We used 454 pyrosequencing to sequence 16S ribosomal RNA (rRNA) gene amplicons for DNA prepared from eight diverse, inbred A. thaliana accessions. Plants were grown from surface-sterile seeds in climate-controlled conditions in two diverse soils, respectively termed Mason Farm and Clayton (Supplementary Table 1; detailed in Supplementary Information). For each soil, we assayed multiple individuals from each A. thaliana accession grown from sterile seeds in both soils across independent full-factorial biological replicates, in which all genotypes and bulk soils (pots without a plant) for a given soil type were grown in parallel (Supplementary Table 2). We isolated separate rhizosphere and EC fractions from individual plant root systems (Supplementary Fig. 1 and Supplementary Table 2). We established 1114F and 1392R as our primer pair (Supplementary Information and Supplementary Fig. 2). Using an otupipe-based pipeline (http://drive5.com/otupipe/), we grouped sequences into 97%-identical operational taxonomic units (OTUs), reduced noise and removed chimaeras. We determined technical reproducibility thresholds to conclude that OTUs defined by $25 reads in $5 samples (hereafter 25 3 5) are individually 'measurable OTUs' 16,17 (Supplementary Figs 2 and 10). All data reported here are from one run of our otupipe-based pipeline (Supplementary Fig. 3 and Supplementary Database 1).

Excluding additional control samples, we ribotyped 1,248 samples comprising 111 bulk soil, 613 rhizosphere and 524 EC samples, generating 9,787,070 high-quality reads (Supplementary Figs 3 and4a-c). After removing plant-sequence-derived OTUs, we obtained a table of usable OTU read counts per sample containing 6,387,407 reads distributed across 18,783 OTUs. We normalized this table of Alpha-diversity § Diversity within one sample/ecosystem (usually calculated at feature level) § Alpha-diversity indices q Richness represents the number of species observed (S obs ) q Chao1 estimates total richness (S 1 ) q Pielou's evenness provide information about equity in species abundance q Shannon provides information about both richness and evenness (H') H' = -Σ p i ln p i Sobs i=1 p i proportion of individuals belonging to the i th species 54

Alpha-diversity

Anouk Zancarini -Tools in molecular data analysis -March 2021 § Diversity within one sample/ecosystem (usually calculated at feature level) § Alpha-diversity indices q Richness represents the number of species observed (S obs ) q Chao1 estimates total richness (S 1 ) q Pielou's evenness provide information about equity in species abundance q Shannon provides information about both richness and evenness (H') § Statistical tests q Normal distribution: t-test or ANOVA q No normal distribution: Mann Whitney or Kruskal Wallis 55

Alpha-diversity

Anouk Zancarini -Tools in molecular data analysis -March 2021 Ø D i v e r s i t y w i t h i n o n e s a m p l e / e c o s y s t e m Ø C h a o 1 = t o t a l r i c h n e s s Ø E v e n n e s s = e q u i t y i n f e a t u r e a b u n d a n c e Ø S h a n n o n < = r i c h n e s s a n d e v e n n e s s

Ø S e q u e n c i n g d e p t h = > d i d I c a t c h a l l t h e d i v e r s i t y ?

Alpha-diversity

Anouk Zancarini -Tools in molecular data analysis -March 2021

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition

Learning objectives

Occurrence table ~100 samples ~10,000 features § n << p § Sparse data (~80% of 0)

Filter the data in order to decrease low quality or uninformative features 59

Microbiota data properties

Anouk Zancarini -Tools in molecular data analysis -March 2021

Is a zero value a true zero, meaning that this feature is not present in the sample?

N O T A l w a y s !

Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation 1-3 . Colonization of the root occurs despite a sophisticated plant immune system 4,5 , suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host genotype to vary between inbred Arabidopsis accessions. We describe different bacterial communities in two geochemically distinct bulk soils and in rhizosphere and endophytic compartments prepared from roots grown in these soils. The communities in each compartment are strongly influenced by soil type. Endophytic compartments from both soils feature overlapping, low-complexity communities that are markedly enriched in Actinobacteria and specific families from other phyla, notably Proteobacteria. Some bacteria vary quantitatively between plants of different developmental stage and genotype. Our rigorous definition of an endophytic compartment microbiome should facilitate controlled dissection of plantmicrobe interactions derived from complex soil communities. Roots influence the rhizosphere by altering soil pH, soil structure, oxygen availability, antimicrobial concentration, and quorum-sensing mimicry, and by providing an energy source of dead root material and carbon-rich exudates 6,7 . The microbiota inhabiting this niche can both benefit and undermine plant health; shifting this balance is of agronomic interest. Mutualistic microbes may provide the plant with physiologically accessible nutrients and phytohormones that improve plant growth, may suppress phytopathogens or may help plants withstand heat, salt and drought 8,9 . The rhizosphere community is a subset of soil microbes that are subsequently filtered via niche utilization attributes and interactions with the host to inhabit the endophytic compartment 10 (EC). Although a variety of microbes may enter and become transient endophytes, those consistently found inside roots are Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and chimaeric amplicons. We developed a robust experimental system to sample repeatedly the root microbiome using high-throughput sequencing. Our results confirm many of the general conclusions from earlier studies and, because of controlled experimental design and the power of deep sequencing, provide a key step towards the definition of this microbiome's functional capacity and the host genes that potentially contribute to microbial association phenotypes. Such plant genes would constitute major agronomic targets.

We used 454 pyrosequencing to sequence 16S ribosomal RNA (rRNA) gene amplicons for DNA prepared from eight diverse, inbred A. thaliana accessions. Plants were grown from surface-sterile seeds in climate-controlled conditions in two diverse soils, respectively termed Mason Farm and Clayton (Supplementary Table 1; detailed in Supplementary Information). For each soil, we assayed multiple individuals from each A. thaliana accession grown from sterile seeds in both soils across independent full-factorial biological replicates, in which all genotypes and bulk soils (pots without a plant) for a given soil type were grown in parallel (Supplementary Table 2). We isolated separate rhizosphere and EC fractions from individual plant root systems (Supplementary Fig. 1 and Supplementary Table 2). We established 1114F and 1392R as our primer pair (Supplementary Information and Supplementary Fig. 2). Using an otupipe-based pipeline (http://drive5.com/otupipe/), we grouped sequences into 97%-identical operational taxonomic units (OTUs), reduced noise and removed chimaeras. We determined technical reproducibility thresholds to conclude that OTUs defined by $25 reads in $5 samples (hereafter 25 3 5) are individually 'measurable OTUs' 16,17 (Supplementary Figs 2 and 10). All data reported here are from one run of our otupipe-based pipeline (Supplementary Fig. 3 and Supplementary Database 1).

Excluding additional control samples, we ribotyped 1,248 samples comprising 111 bulk soil, 613 rhizosphere and 524 EC samples, generating 9,787,070 high-quality reads (Supplementary Figs 3 and4a-c). After removing plant-sequence-derived OTUs, we obtained a

Data filtering Raw occurrence data

Alpha-diversity § Different normalisation methods available (depend on your downstream analysis)

q Total Sum Normalisation: dividing the reads for each OTU in a sample by the total number of reads in that sample and multiplying by 100

seq_1

Microbiota data normalisation

Anouk Zancarini -Tools in molecular data analysis -March 2021 § Different normalisation methods available q Total Sum Normalisation: dividing the reads for each OTU in a sample by the total number of reads in that sample and multiplying by 100 q Rarefy: randomly subsampling each sample to the lowest read depth of any sample LETTER doi:10.1038/nature11237

Defining the core Arabidopsis thaliana root microbiome Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11 Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremediation 1-3 . Colonization of the root occurs despite a sophisticated plant immune system 4,5 , suggesting finely tuned discrimination of mutualists and commensals from pathogens. Genetic principles governing the derivation of host-specific endophyte communities from soil communities are poorly understood. Here we report the pyrosequencing of the bacterial 16S ribosomal RNA gene of more than 600 Arabidopsis thaliana plants to test the hypotheses that the root rhizosphere and endophytic compartment microbiota of plants grown under controlled conditions in natural soils are sufficiently dependent on the host to remain consistent across different soil types and developmental stages, and sufficiently dependent on host Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and chimaeric amplicons. We developed a robust experimental system to sample repeatedly the root microbiome using high-throughput sequencing. Our results confirm many of the general conclusions from earlier studies and, because of controlled experimental design and the power of deep sequencing, provide a key step towards the definition of this microbiome's functional capacity and the host genes that potentially contribute to microbial association phenotypes. Such plant genes would constitute major agronomic targets.

We used 454 pyrosequencing to sequence 16S ribosomal RNA (rRNA) gene amplicons for DNA prepared from eight diverse, inbred A. thaliana accessions. Plants were grown from surface-sterile seeds in Ø Rarefied at 1000 reads per sample

Microbiota data normalisation

Anouk Zancarini -Tools in molecular data analysis -March 2021 REMARK: When the sequencing depth is not enough and you have big differences in library sizes (~x10), it is better to rarefy your data than calculate percentage q Total Sum Normalisation: dividing the reads for each OTU in a sample by the total number of reads in that sample and multiplying by 100 q Rarefy: randomly subsampling each sample to the lowest read depth of any sample q DESeq-VS: a variance stabilizing transformation (used for RNA-seq analysis) q edgeR-TMM: a trimmed mean of M-values normalisation

R ES EAR CH

Open Access

Normalization and microbial differential abundance strategies depend upon data characteristics

Sophie Weiss 1 , Zhenjiang Zech Xu 2 , Shyamal Peddada 3 , Amnon Amir 2 , Kyle Bittinger 4 , Antonio Gonzalez 2 , Catherine Lozupone 5 , Jesse R. Zaneveld 6 , Yoshiki Vázquez-Baeza 7 , Amanda Birmingham 8 , Embriette R. Hyde 2 and Rob Knight 2,7,9* Abstract Background: Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems.

Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses.

Results: Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination Ø D i f f e r e n t n o r m a l i s a t i o n m e t h o d s f o r s a m p l e c o m p a r i s o n • F o r c o m m u n i t y l e v e l a n a l y s i s ( T S N o r r a r e f y i n g )

• F o r d i f f e r e n t i a l a b u n d a n c e t e s t i n g ( D E S e q -V S o r e d g e R -T M M )

Ø B e t t e r t o u s e r a r e f y i n g w h e n s e q u e n c i n g d e p t h i s n o t e n o u g h a n d t h e r e a r e b i g d i f f e r e n c e s i n l i b r a r y s i z e s

Microbiota data normalisation

Anouk Zancarini -Tools in molecular data analysis -March 2021

In the tutorial, look at: o 5. Data filtering and normalisation

Practice time: data filtering and normalisation Tutorial link:

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition

Learning objectives

Process overview

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition Land plants associate with a root microbiota distinct from the complex microbial community present in surrounding soil. The microbiota colonizing the rhizosphere (immediately surrounding the root) and the endophytic compartment (within the root) contribute to plant growth, productivity, carbon sequestration and phytoremedia-Microbial community structure differs across plant species 12,13 , and there are reports of host-genotype-dependent differences in patterns of microbial associations 14,15 . However, the divergent methods used in those studies relied on small sample sizes and low-resolution phylotyping techniques potentially confounded by off-target sequences and

Microbiota data analysis

q Define microbiome and state microbiome importance q Identify differences between metabarcoding and metagenomics q Explain how microbiota data are generated (including bias) q Explain and preform data pre-processing q Explain how microbiota data are analysed q Define, perform and interpret alpha-diversity q Address sparsity, under-sampling and uneven sampling depth using data filtering and normalization q Define, perform and interpret beta-diversity q Generate and interpret multivariate data analyses q Perform and interpret appropriate statistical tests q Visualize and interpret microbial community composition

Learning objectives

Microbiota data analysis assignment

For

  § Gene ubiquitous § With conserve and variable regions For Bacteria analysis: 16S rRNA gene § Gene code for a RNA part of the ribosome

  , Jessica (2014): Overview of data generation, processing and analysis using QIIME. Figshare. https://doi.org/10.6084/m9.figshare.902219Tools in molecular data analysis -March 2021

  e. assign a sequence to a sample) § Remove adaptor and barcode § Remove low quality reads (i.e. filtering step) sequences to microbiota data sets § De-multiplex (i.e. assign a sequence to a sample) § Remove adaptor and barcode § Remove low quality reads (i.e. filtering step) § Remove chimeras 31 Anouk Zancarini -Tools in molecular data analysis -March 2021 During PCR multiple sequence can combine to form a hybrid Chimeras must be removed sequences to microbiota data sets § De-multiplex (i.e. assign a sequence to a sample) § Remove adaptor and barcode § Remove low quality reads (i.e. filtering step) sequences to microbiota data sets § De-multiplex (i.e. assign a sequence to a sample) § Remove adaptor and barcode § Remove low quality reads (i.e. filtering step) § Remove chimeras § Merged pair-end reads § Sequence clustering in

  DADA2 implements a new quality-aware model of Illumina amplicon errors. Sample composition is inferred by dividing amplicon reads into partitions consistent with sequences to microbiota data sets Anouk Zancarini -Tools in molecular data analysis -March 2021 Sequencing data Pre-processing § A new pre-processing pipeline DADA2 § Using Divisive Amplicon Denoising Algorithm (DADA) to correct amplicon errors without constructing OTU (i.e. Amplicon Sequence Variants or ASV)

  assignTaxonomy() assign taxonomy to the ASV Step 2: From sequences to microbiota data sets Anouk Zancarini -Tools in molecular data analysis -March 2021 Sequencing data SILVA database h i g h q u a l i t y r i b o s o m a l R N A d a t a b a s e s LETTER doi:10.1038/nature11237 Defining the core Arabidopsis thaliana root microbiome Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11

  Tools in molecular data analysis -March 2021 Alpha-diversity § Diversity within one sample/ecosystem § Alpha-diversity indices q Richness represents the number of species observed (S obs ) Tools in molecular data analysis -March 2021 https://bigdata_microbiome.presenterswall.nl/ Alpha-diversity Anouk Zancarini -Tools in molecular data analysis -March 2021 Bulk soil Rhizosphere Endosphere LETTER doi:10.1038/nature11237 Defining the core Arabidopsis thaliana root microbiome Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11

  Diversity within one sample/ecosystem (usually calculated at feature level) § Alpha-diversity indices q Richness represents the number of species observed (S obs ) q Chao1 estimates total richness (S 1 ) S 1 = S obs + ___ F in molecular data analysis -March 2021 REMARK: Difference between observed richness and Chao1give you information about the sequencing depth (enough if Richness = Chao1; not enough if Richness << Chao1) REMARK: Chao1 can only be calculated on raw data § Diversity within one sample/ecosystem (usually calculated at feature level) § Alpha-diversity indices q Richness represents the number of species observed (S obs ) q Chao1 estimates total richness (S 1 ) q Pielou's evenness provide information about equity in species abundance 53

Ø

  S h o u l d b e c a l c u l a t e d o n r a w d a t a Ø O b s e r v e d r i c h n e s s = n u m b e r o f f e a t u r e s o b s e r v e d

ØIn

  M i c r o b i o t a d a t a u s u a l l y s p a r s e = > n e e d f i l t e r i n g e s p e c i a l l y w h e n s e q u e n c i n g d e p t h w a s n o t e n o u g h Ø U n e v e n l i b r a r y s i z e = > n e e d n o r m a l i s a t i o n f o r s a m p l e c o m p a r i s o nMicrobiota data propertiesAnouk Zancarini -Tools in molecular data analysis -March 2021 microbiota data sets to data visualisation Anouk Zancarini -Tools in molecular data analysis -March 2021

Ø

  D i v e r s i t y b e t w e e n t w o s a m p l e s / e c o s y s t e m s Ø D i f f e r e n t d i s t a n c e m e a s u r e m e n t s :• J a c c a r d ( o c c u r r e n c e t a b l e : p r e s e n c e / a b s e n c e )• B r a y -C u r t i s ( o c c u r r e n c e t a b l e : a b u n d a n c e )• U n i f r a c ( o c c u r r e n c e t a b l e a n d p h y l o g e n y ) Ø V i s u a l i s a t i o n u s i n g o r d i n a t i o n p l o t ( P C O A )Anouk Zancarini -Tools in molecular data analysis -March 2021 scienceparkstudygroup.github.io/mic robiome-lesson/06-beta-diversity/index.htmlAnouk Zancarini -Tools in molecular data analysis -March 2021

  Tools in molecular data analysis -March 2021 LETTER doi:10.1038/nature11237 Defining the core Arabidopsis thaliana root microbiome Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11

Figure 2 |

 2 Figure 2 | OTUs that differentiate the rhizosphere from soil. A, Heat map sho counts from the rarefied OTU table (Supplementary Database 2a; log 2 -tran from each of the 256 rhizosphere-and differentiating OTUs present across rep Samples and OTUs are clustered on th Curtis similarities (group-average linkag relates colours to the untransformed re Different hues of the same colour corre different replicates as in Fig. 1. B, The GLMM predictions (best linear unbiase predictors) is represented by bar heigh predicted as EC enriched (red, up) or E (blue, down). b, OTUs higher in the EC Farm soil than Clayton (brown, up) or Clayton soil than Mason Farm (gold, do in a that are not differentially affected b are shown there in darker hues. c, OTU as rhizosphere enriched (as in a). d, OTU rhizosphere in one soil type (as in b). C, Histograms showing the distribution present in the 778 measurable OTUs in

  Bacteria § Co-occurrence analyses § Functional prediction (e.g. PICRUST) § New sequencing technologies q Long reads for a better identification q Tools in molecular data analysis -March 2021 § Scientific context, research question and experimental design § Data properties (i.e. sparsity and library size) § Data filtering and normalisation § Alpha-diversity § Beta-diversity § Microbial composition § Conclusion

data Observation metadata 37 Step 2: From sequences to microbiota data sets

			Taxonomic assignment
	§ Looking for sequence homology with ref databases Data sets output Example of the bacteria Escherichia coli O157:H7 Process overview Example of the bacteria Escherichia coli O157:H7 -> ASV_6287 § Accuracy depends on quality and completeness of the database § Sample metadata § Occurrence data Sequencing data Domain Bacteria Domain Bacteria
	Check quality Check quality		Sample metadata § Observation metadata (taxonomic assignation) Kingdom Eubacteria Kingdom Eubacteria
	Filtering Filtering		Phylum Phylum	Proteobacteria Proteobacteria
	Merging Denoising ASV table Denoising ASV table Merging	~100 samples	Class Class Order Order	Gammaproteobacteria Gammaproteobacteria Enterobacterales Enterobacterales
	Chimeras Chimeras		Ribosomal Data Project database Family Family	Enterobacteriaceae Enterobacteriaceae	Greengenes database
	removal Taxonomy removal Taxonomy		Genus Genus	Escherichia-Shigella Undefined
	assignation assignation		Species Species	Escherichia coli Undefined
	Microbiota data		Strain	O157:H7	38 39

~10,000 features Occurrence

from sequences to microbiota data sets Script on Canvas or link:

	o Getting ready
	o Inspect read quality profiles
	o Filter and trim
	o Learn the error rates
	o Sample inference
	o Merge paired reads
	o Construct sequence table
	o Remove chimeras
	o Track reads through the pipeline
	o Assign taxonomy
	Tutorial link:

Anouk Zancarini -Tools in molecular data analysis -March 2021

In the tutorial, look at: http://benjjneb.github.io/dada2/tutorial.html

Practice time:

https://scienceparkstudygroup.github.io/microbiomelesson/02-data-preprocess-fastq-to-asv/index.html

number of reads per sample 65 Microbiota data properties: library size per sample

  table of usable OTU read counts per sample containing 6,387,407 reads distributed across 18,783 OTUs. We normalized this table of Library size is the total

	§
		seq_1 seq_2 seq_3	(…)	seq_p total_reads
	sample_1 500	80	20		5	10,000
	sample_2 500	80	20		5	1,000
	sample_3	50	8	2		0	1,000
	(…)
	sample_n 2000	0	2		0	10,000

Anouk Zancarini -Tools in molecular data analysis -March 2021 https://bigdata_microbiome.presenterswall.nl/

  dividing the reads for each OTU in a sample by the total number of reads in that sample and multiplying by 100 q Rarefy: randomly subsampling each sample to the lowest read depth of any sample

		Microbiota data normalisation Microbiota data normalisation Microbiota data normalisation Microbiota data normalisation Microbiota data normalisation
	q Total Sum Normalisation: dividing the reads for each OTU in a sample by q Total Sum Normalisation: dividing the reads for each OTU in a sample by q Total Sum Normalisation: dividing the reads for each OTU in a sample by q Total Sum Normalisation: dividing the reads for each OTU in a sample by
	the total number of reads in that sample and multiplying by 100 the total number of reads in that sample and multiplying by 100 the total number of reads in that sample and multiplying by 100 the total number of reads in that sample and multiplying by 100
	seq_2 seq_3 seq_1 seq_2 seq_3 seq_1 seq_2 seq_3 q Rarefy: randomly subsampling each sample to the lowest read depth of (…) seq_p total_reads (…) seq_p total_reads (…) q Rarefy: randomly subsampling each sample to the lowest read depth of seq_p total_reads any sample any sample
	sample_1 500 sample_1 500 sample_1 500	80 80 80	20 20 20		5 5 5	10,000 10,000 10,000
	sample_2 500 sample_2 500 sample_2 500 seq_1 seq_2 seq_3 80 20 80 20 80 20 seq_1 seq_2 seq_3 seq_1 seq_2 seq_3	(…) (…) (…)	5 5 5 seq_p total_reads 1,000 1,000 1,000 seq_p total_reads seq_p total_reads
	sample_3 sample_3 sample_3 sample_1 500 50 50 50 sample_1 500 sample_1 500	8 8 8 80 80 80	2 2 2 20 20 20		0 0 0 5 5 5	1,000 1,000 1,000 10,000 10,000 10,000
	sample_2 500 sample_2 500 sample_2 500	80 80 80	20 20 20		5 5 5	1,000 1,000 1,000
	sample_3 sample_3 sample_3	50 50 50	8 8 8	2 2 2		0 0 0	1,000 1,000 1,000
		seq_1 seq_2 seq_3 seq_1 seq_2 seq_3 seq_1 seq_2 seq_3	(…) (…) (…)	seq_p total_reads seq_p total_reads seq_p total_reads
	sample_1 sample_2 sample_3 sample_1 sample_2 sample_1 0.05 seq_1 seq_2 seq_3 0.008 0.002 seq_1 seq_2 seq_3 seq_1 seq_2 seq_3 sample_3 sample_2 0.5 0.08 0.02 sample_1 sample_1 sample_1 52 8 1 sample_3 0.05 0.008 0.002 sample_2 sample_2 500 80 20 sample_2 500 80 20	(…) (…) (…)	100 100 seq_p total_reads 0.0005 seq_p total_reads seq_p total_reads 100 0.005 100 1,000 1,000 0 1,000 100 0 100 1,000 5 1,000 5 1,000
	sample_3 sample_3 sample_3	50 50	8 8	2 2		0 0	1,000 1,000 1,000
							69 70 71 72 73 74

Anouk Zancarini -Tools in molecular data analysis -March 2021 § Different normalisation methods available Anouk Zancarini -Tools in molecular data analysis -March 2021 § Different normalisation methods available Anouk Zancarini -Tools in molecular data analysis -March 2021 § Different normalisation methods available Anouk Zancarini -Tools in molecular data analysis -March 2021 § Different normalisation methods available Anouk Zancarini -Tools in molecular data analysis -March 2021 § Different normalisation methods available q Total Sum Normalisation:

  Scientific context, research question and experimental design § Data properties (i.e. sparsity and library size) § Data filtering and normalisation § Alpha-diversity § Beta-diversity § Microbial composition § Conclusion § Rmarkdown report in pdf § Think about reproducibility • What have you done? • Why? § Include, describe and interpret your plots & statistical results

	D e t a i l e d i n s t r u c t i o n s a v a i l a b l e o n C a n v a s

104

Anouk Zancarini -Tools in molecular data analysis -March 2021 §

Derek S. Lundberg 1,2 *, Sarah L. Lebeis 1 *, Sur Herrera Paredes 1 *, Scott Yourstone 1,3 *, Jase Gehring 1 , Stephanie Malfatti 4 , Julien Tremblay 4 , Anna Engelbrektson 4 {, Victor Kunin 4 {, Tijana Glavina del Rio 4 , Robert C. Edgar 5 , Thilo Eickhorst 6 , Ruth E. Ley 7 , Philip Hugenholtz 4,8 , Susannah Green Tringe 4 & Jeffery L. Dangl 1,2,9,10,11

Anouk Zancarini -Tools in molecular data analysis -March 2021

https://scienceparkstudygroup.github.io/ microbiome-lesson/03-data-explorationand-properties/index.html Anouk Zancarini -Tools in molecular data analysis -March 2021

https://scienceparkstudygroup.github.io/ microbiome-lesson/05-data-filtering-andnormalisation/index.html Anouk Zancarini -Tools in molecular data analysis -March 2021

How microbiota data are generated?

Test your knowledge…

• Please answer the 3 questions in the following quiz https://bigdata_microbiome.presenterswall.nl/

Sequencing depth

Anouk Zancarini -Tools in molecular data analysis -March 2021 REMARK: If the sequencing depth is not enough, it will be difficult to compare difference between samples for low counts. Therefore, it will be better to remove features that have only low counts.

Process overview

Data filtering Raw occurrence data 61

Step 3: From microbiota data sets to data visualisation Beta-diversity § Diversity between two samples/ecosystems (feature level) § Calculate distances between samples q Jaccard (presence/absence in occurrence table) q Bray-Curtis (occurrence table) q Unifrac (occurrence table and phylogeny)

q Unweighted q Weighted 84 Anouk Zancarini -Tools in molecular data analysis -March 2021

Beta-diversity § Diversity between two samples/ecosystems (feature level) § Calculate distances between samples § Visualisation (ordination plot) Lundberg et al. 2012 than 50% senescent leaves). Additional control samples from the reference genotype Col-0 harvested from four independent digs of Mason Farm soil underscored the reproducibility of these bacterial community profiles (Supplementary Fig. 6). Together, these data demonstrate that the interaction of diverse soil communities with plants determines the assembly of the rhizosphere, leading to biome. By con extent in one so Importantly, 3 blue bars). So significantly ov hypergeometri (Fig. 2B,c than 50% senescent leaves). Additional control samples from the reference genotype Col-0 harvested from four independent digs of Mason Farm soil underscored the reproducibility of these bacterial community profiles (Supplementary Fig. 6). Together, these data demonstrate that the interaction of diverse soil communities with plants determines the assembly of the rhizosphere, leading to biome. B extent in Importan blue bar significan hypergeo (Fig. 2B Fig. 5b). Samples harvested at different developmental stages clustered together, indicating that this variable does not have a major effect on overall community composition (Fig. 1 and Supplementary Fig. 5a,b; yng versus old, where yng refers to the time of appearance of an inflorescence meristem and old refers to fruiting plants with greater than 50% senescent leaves). Additional control samples from the reference genotype Col-0 harvested from four independent digs of Mason Farm soil underscored the reproducibility of these bacterial community profiles (Supplementary Fig. 6). Together, these data demonstrate that the interaction of diverse soil communities with plants determines the assembly of the rhizosphere, leading to and Supplementary tary Fig. 5). Of these a; dark and light red these 164, 97 were (Fig. 2B Beta-diversity § Diversity between two samples/ecosystems (feature level) § Calculate distances between samples § Visualisation (ordination plot) § Statistical comparison among sets of communities q PERMANOVA: ANOVA type method based on sample to sample distances to compare within and between group distances & P-value by permutation q ANOSIM: Similar to Permanova, but analysis is performed on ranked distances 92 Anouk Zancarini -Tools in molecular data analysis -March 2021

Beta-diversity


Christopher Burot

Patricia Bonin

Gwénola Simon

Laurie Casalot

Jean-François Rontani
email: jean-francois.rontani@mio.osupytheas.fr

Bonin Burot Christopher

Simon Gwénola

Casalot Laurie

Rontani Jean-François

Singlet oxygen-induced alteration of bacteria associated with phytodetritus: effect of irradiance

Keywords: algae, light, molecular Senescent phytoplankton, Attached bacteria, Type-II photosensitized oxidation, Irradiance effect, Arctic, Sinking particulate matter, Sea ice Senescent phytoplankton, Attached bacteria, Type II photosensitized oxidation, Irradiance effect, Arctic, Sinking particulate matter, Sea ice

Introduction

Type II photooxidation processes are light-induced indirect degradation reactions, that involve the action of a photosensitizer molecule (typically pigments or aromatic compounds) in its triplet state [START_REF] Gollnick | Type II Photooxygenation Reactions in Solution[END_REF]. Chlorophyll, which is an efficient photosensitizer [START_REF] Foote | Photosensitized oxidation and singlet oxygen: Consequences in biological systems[END_REF], can trigger type II photooxidation processes in phytoplanktonic cells, especially in late bloom phases when they are senescent [START_REF] Nelson | Rates and possible mechanism of light-dependent degradation of pigments in detritus derived from phytoplankton[END_REF]. Indeed, in senescent cells, the cessation of photosynthetic reactions results in an accelerated rate of formation of 3 Chl and reactive oxygen species (ROS), mainly singlet oxygen ( 1 O 2 ) [START_REF] Knox | Singlet oxygen and plants[END_REF][START_REF] Nelson | Rates and possible mechanism of light-dependent degradation of pigments in detritus derived from phytoplankton[END_REF]. The rate of formation of these potentially damaging species can then exceed the quenching capacity of the photoprotective system, thus enabling the photodegradation of cell components (photodynamic effect) to occur [START_REF] Merzlyak | Free radical metabolism, pigment degradation and lipid peroxidation in leaves during senescence[END_REF]. 1 O 2 reacts with a broad range of cellular compounds but especially unsaturated lipids such as sterols and unsaturated fatty acids [START_REF] Rontani | Photo-and autoxidation of unsaturated algal lipids in the marine environment: An overview of processes, their potential tracers, and limitations[END_REF], proteins [START_REF] Morgan | Protective mechanisms against peptide and protein peroxides generated by singlet oxygen[END_REF] and nucleic acids [START_REF] Agnez-Lima | Mutation Spectrum Induced by Singlet Oxygen in Escherichia coli Deficient in Exonuclease III[END_REF][START_REF] Ravanat | Singlet Oxygen Induces Oxidation of Cellular DNA[END_REF]. The very high reactivity of 1 O 2 with these biomolecules is due to the lack of spin restriction that normally prevents 3 O 2 from reacting with these compounds [START_REF] Zolla | Involvement of active oxygen species in degradation of light-harvesting proteins under light stresses[END_REF].

The 1 O 2 thus formed reacts not only with membrane components of phytoplankton but also with the monounsaturated fatty acids (MUFAs) of its neighboring attached bacteria [START_REF] Rontani | On the origin of cis-vaccenic acid photodegradation products in the marine environment[END_REF][START_REF] Christodoulou | Comparative study of UV and visible light induced degradation of lipids in non-axenic senescent cells of Emiliania huxleyi[END_REF][START_REF] Petit | Photochemical Production and Behavior of Hydroperoxyacids in Heterotrophic Bacteria Attached to Senescent Phytoplanktonic Cells[END_REF][START_REF] Petit | Transfer of singlet oxygen from senescent irradiated phytoplankton cells to attached heterotrophic bacteria: Effect of silica and carbonaceous matrices[END_REF]. Indeed, in phototrophic cells, the radius of the sphere of activity of 1 O 2 from its point of production was estimated to be between 155 and 340 nm [START_REF] Baier | Time-resolved investigations of singlet oxygen luminescence in water, in phosphatidylcholine, and in aqueous suspensions of phosphatidylcholine or HT29 cells[END_REF][START_REF] Ogilby | Singlet oxygen: there is indeed something new under the sun[END_REF][START_REF] Skovsen | Lifetime and Diffusion of Singlet Oxygen in a Cell[END_REF], which is a sufficient distance to allow it to cross the cellular membranes [START_REF] Ogilby | Singlet oxygen: there is indeed something new under the sun[END_REF] and thus attack attached bacteria.

It has previously been demonstrated that low solar irradiance favors slow production and diffusion of 1 O 2 through the membranes and thus increases photooxidative damage of the unsaturated algal lipids [START_REF] Amiraux | Paradoxical effects of temperature and solar irradiance on the photodegradation state of killed phytoplankton[END_REF]. However, the effects of intensity of solar irradiance on the photooxidation of bacteria attached to phytodetritus are totally unknown.

Here, to clarify this issue, we incubated senescent cells of the diatom Thalassiosira sp. (a phytoplankton genus widely distributed in oceans, [START_REF] Malviya | Insights into global diatom distribution and diversity in the world's ocean[END_REF] in association with the bacteria Pseudomonas stutzeri under contrasted artificial light irradiances. Bacteria of the Pseudomonas genus were selected because they are often associated with Thalassiosira cells [START_REF] Bidle | Bacterial control of silicon regeneration from diatom detritus: significance of bacterial ectohydrolases and species identity[END_REF][START_REF] Schaum | Enhanced biofilm formation aids adaptation to extreme warming and environmental instability in the diatom Thalassiosira pseudonana and its associated bacteria[END_REF]. To validate the results, we compared the photooxidation state of diatoms and their attached bacteria in numerous samples of Arctic sea ice and sinking particulate matter. Samples were collected at different depths under open water. marginal ice zone and first-year ice).

Material and methods

Collection of in situ samples

Detailed descriptions of the Arctic marine particulate matter and sea ice sample collection processes (e.g. sampling dates, depths, volumes, etc.) can be found in [START_REF] Rontani | Intense photooxidative degradation of planktonic and bacterial lipids in sinking particles collected with sediment traps across the Canadian Beaufort Shelf (Arctic Ocean)[END_REF]Rontani et al. ( , 2016[START_REF] Rontani | Control of the preservation of sympagic algal material in surficial sediments of central and eastern Baffin Bay by bactericidal hydroperoxides and free fatty acids[END_REF] and [START_REF] Amiraux | Stress factors resulting from the Arctic vernal sea-ice melt: Impact on the viability of bacterial communities associated with sympagic algae[END_REF]. Briefly, sinking particles were collected i) in Resolute Passage under first-year ice at 5 m and 30 m depth with Hydro-Bios MS12model multi-sediment traps [START_REF] Rontani | Monitoring abiotic degradation in sinking versus suspended Arctic sea ice algae during a spring ice melt using specific lipid oxidation tracers[END_REF], ii) in central Baffin Bay under the marginal ice zone at 25 m depth with a Technicap PPS4 drifting sediment trap [START_REF] Rontani | Control of the preservation of sympagic algal material in surficial sediments of central and eastern Baffin Bay by bactericidal hydroperoxides and free fatty acids[END_REF], and iii) in the Beaufort Sea in open water at 100 m depth with Technicap PPS traps [START_REF] Rontani | Intense photooxidative degradation of planktonic and bacterial lipids in sinking particles collected with sediment traps across the Canadian Beaufort Shelf (Arctic Ocean)[END_REF]. Sea-ice samples were collected at GreenEdge Project landfast ice station located near Broughton Island in the Davis Strait using a Kovacs Mark V 14-cmdiameter corer and focusing on the bottommost 10 cm of sea ice [START_REF] Amiraux | Stress factors resulting from the Arctic vernal sea-ice melt: Impact on the viability of bacterial communities associated with sympagic algae[END_REF] (Fig. 1).

Algal phytodetritus production

The non-axenic diatom Thalassiosira sp. RCC1714 was obtained from the Roscoff culture collection. Diatoms were grown in F/2 medium at 17°C in a constant-environment cabinet under an irradiance of 18 W.m -2 (Osram, Lumilux 12:12h light/dark cycle). After two months of growth, cell density was measured by counting under microscope, and death of the algal cells was induced by sonication. Thalassiosira sp. cells were sonicated on ice using a Branson Sonifier 450 ultrasound generator equipped with a Branson S450A cell disruptor probe at 240 W for 10 minutes (duty cycle 50%). The sonicated cells were then sub-sampled in 50 mL Falcon tubes and stored at -20°C for later photooxidation experiments.

Culturing of P. stutzeri

Pre-culture of P. stutzeri ATCC 14405 was grown aerobically overnight on a rotary shaker in the dark at 17°C on marine broth medium (US Biological™) diluted 1:10 with sterilized artificial sea water [START_REF] Kester | Preparation of Artificial Seawater[END_REF] therefore not adding large amounts of assimilable organic matter from the inoculum in the further steps. The culture was inoculated with the aerobic pre-culture to obtain an initial OD 620nm , about 0.020. At the mid growth exponential phase, the incubation was stopped and cells counted based on epifluorescence in the presence of fluorochrome (4P,6-diamidino-2P-phenylindole dihydrochloride, DAPI) as described in [START_REF] Otto | DAPI staining of fixed cells for high-resolution flow cytometry of nuclear DNA[END_REF].

Photooxidation experiments

Phytodetritus and bacteria were mixed (ratio algal:bacterial cells = 1:5) in K medium [START_REF] Keller | Media for the culture of oceanic ultraphytoplankton[END_REF] to obtain a final volume of 100 mL. The experiments were performed in triplicate. After taking three 10 mL sub-samples (controls before irradiation) the reactional systems were exposed to high or low irradiance.

High irradiance experiments

The high-irradiance experiments were carried out in sterilized quartz and glass tubes (100 mL) with an Altas Suntest CPS + solar simulator equipped with a Xenon NXE 1500 lamp producing a high solar irradiance (500 W.m -2 ) in the 300-800 nm range, i.e. close to that generally observed at noon at the surface of the Mediterranean Sea [START_REF] Sarra | Assessing the quality of shortwave and longwave Irradiance observations over the ocean: one year of high-time-resolution measurements at the Lampedusa oceanographic observatory[END_REF] and not far from the one observed at the surface of Arctic waters in summer (up to 350 W m -2 , [START_REF] Horvat | The Effect of Melt Pond Geometry on the Distribution of Solar Energy Under First-Year Sea Ice[END_REF]. To eliminate UV radiation, experiments were also performed in glass tubes reducing the irradiance to 446 W.m -2 . After irradiation with a light dose of 1000 KJ.m -2 (corresponding to an irradiation time of 0.5 and 15 h at the surface and at the deep chlorophyll maximum (DCM) of the Mediterranean Sea, respectively), 10 mL was collected. All the samples were filtered on 0.8 µm glass fiber filters (GF/F) to concentrate bacteria attached on phytodetritus and stored at -20°C until further lipid extraction.

Controls were also conducted in triplicate with no phytodetritus. Bacteria (P. stutzeri) were mixed with sterilized artificial sea water complemented with 0.1 g.L -1 of lactate/acetate (50:50) as carbon source, in 100 mL sterilized quartz or glass tubes and exposed to light in the solar simulator for the same duration as the tubes containing phytodetritus and bacteria. After irradiation, 50 mL was sampled and bacteria were concentrated by centrifugation (Beckman & Coulter Allegra X-22 centrifuge at 20,000 g for 30 min). The irradiated bacterial cells were then stored at -20°C until further lipid extraction. 2.4. 2. Low irradiance experiments Low-irradiance photosynthetically active radiation (PAR) (18 W.m -2 ) in the range 400-750 nm, i.e. comparable to that observed at the DCM in the Mediterranean Sea (Maraňón et al., 2020), was obtained with a Binder incubator equipped with Osram Lumilux lamps. The vials were exposed to a light dose of 1000 KJ.m -2 in the incubator at 17°C. After irradiation, 10 mL samples was collected, filtered on 0.8 µm GF/F and stored at -20°C until further lipid extraction. Control experiments without phytodetritus were also carried out as described above.

Lipid extraction and derivatization

Samples (GF/F filters or bacterial pellets) were reduced with excess NaBH 4 after adding MeOH (25 mL, 30 min) to reduce labile hydroperoxides (resulting from photooxidation) to alcohols, which are more amenable to gas chromatography-mass spectrometry (GC-MS) analysis. Water (25 mL) and KOH (2.8 g) were then added and the resulting mixture was saponified by refluxing (2 h), then cooled, acidified (HCl, 2 N) to pH 1, and extracted with dichloromethane (3 × 20 mL). The combined extracts were then concentrated by rotary evaporation at 40°C to give total lipid extracts (TLEs).

Dry TLEs were derivatized by dissolving them in 300 µL of a mixture of pyridine/bis-(trimethylsilyl)trifluoroacetamide (BSTFA; Supelco; 2:1, v/v) and silylated in a heating block (50°C, 1 h). After evaporation to dryness under a stream of N 2 , the derivatized residue was dissolved in ethyl acetate/BSTFA (to avoid desilylation) and analyzed by GC-MS/MS. 2.6. Gas chromatography-tandem mass spectrometry (GC-MS/MS) GC-MS/MS analyses were performed on an Agilent 7890A/7010A tandem-quadrupole gas chromatograph system (Agilent Technologies, Les Ulis, France) using a cross-linked 5% phenyl-methylpolysiloxane (Agilent; HP-5MS ultra inert, 30 m × 0.25 mm, 0.25 µm film thickness) capillary column. Analyses were performed with an injector operating in pulsed splitless mode set at 270°C. Oven temperature was ramped from 70°C to 130°C at 20°C.min - 1 , then to 250°C at 5°C.min -1 and finally to 300°C at 3°C.min -1 . Pressure of the carrier gas (He) was maintained at 0.69 × 10 5 Pa until the end of the temperature program, then ramped from 0.69 × 10 5 Pa to 1.49 × 10 5 Pa at 0.04 × 10 5 Pa.min -1 . The following mass spectrometer conditions were used: electron energy 70 eV, source temperature 230°C, quadrupole 1 temperature 150°C, quadrupole 2 temperature 150°C, collision gas (N 2 ) flow 1.5 mL.min -1 , quench gas (He) flow 2.25 mL.min -1 , mass range 50-700 Daltons, cycle time 313 ms. Vaccenic acid photooxidation products were quantified in multiple reaction monitoring (MRM) mode using the transitions m/z 339 → 131 for the parent vaccenic acid and m/z 199 → 129, m/z 213 → 129, m/z 357 → 129 and m/z 371 → 129 for its oxidation products [START_REF] Rontani | Lipid Oxidation Products: Useful Tools for Monitoring Photo-and Autoxidation in Phototrophs[END_REF]. Precursor ions were selected from the most intense ions and specific fragmentations observed in electron ionization (EI) mass spectra. Collision-induced dissociation (CID) was optimized by using collision energies at 5, 10, 15 and 20 eV. Quantification involved peak integration and determination of individual response factors using standard compounds and Mass Hunter software (Agilent Technologies, Les Ulis, France).

Standard compounds

Vaccenic and palmitoleic acids were obtained from Sigma-Aldrich. Vaccenic acid oxidation was achieved using Fe 2+ /ascorbate [START_REF] Loidl-Stahlhofen | Generation of αhydroxyaldehydic compounds in the course of lipid peroxidation[END_REF]. Subsequent reduction of the resulting hydroperoxides in methanol with excess NaBH 4 afforded the corresponding hydroxyacids. 2.8. Statistical analysis Results of statistical tests were considered to be significant at a confidence level of 95% (α = 0.05). All tests were performed using XL-Stat Statistics software.

Results and Discussion

In vitro experiments

To determine the effect of solar irradiance on the efficiency of transfer of 1 O 2 from phytodetritus to their attached bacteria, senescent Thalassiosira sp. cells associated with P. stutzeri were irradiated by a light dose of 1000 KJ.m -2 under contrasting irradiances, i.e. 18 (PAR), 446 (PAR) and 500 (UV+PAR) W.m -2 . The efficiency of 1 O 2 transfer to bacteria was estimated based on the measured concentration of photooxidation products of vaccenic acid (pmol.L -1 ). Vaccenic acid was present in high proportion in P. stutzeri [START_REF] Rainey | Genome and fatty acid analysis of Pseudomonas stutzeri[END_REF] but only in weak amount in the unknown bacterial community associated with the nonaxenic strain of Thalassiosira sp. employed (amount estimated to be more than one order of magnitude lesser than this present in the cells of P. stutzeri added during the different experiments).

Type-II photosensitized oxidation of vaccenic acid involves the addition of 1 O 2 to the two carbon atoms of its  11 double bond, and leads to the formation of 11hydroperoxyoctadec-12(trans)-enoic and 12-hydroperoxyoctadec-10(trans)-enoic acids [START_REF] Rontani | Lipid Oxidation Products: Useful Tools for Monitoring Photo-and Autoxidation in Phototrophs[END_REF]. These hydroperoxides can subsequently undergo stereoselective radical allylic rearrangement to afford 13-hydroperoxyoctadec-11(trans)-enoic and 10hydroperoxyoctadec-11(trans)-enoic acids, respectively [START_REF] Porter | Mechanisms of free radical oxidation of unsaturated lipids[END_REF]. Trans allylic hydroxy acids arising from NaBH 4 reduction of these hydroperoxyacids are sufficiently stable for use as tracers of type-II photosensitized oxidation processes [START_REF] Marchand | Characterisation of photooxidation and autoxidation products of phytoplanktonic monounsaturated fatty acids in marine particulate matter and recent sediments[END_REF]. Interestingly, we detected only trace amounts of vaccenic acid photooxidation products before irradiation (under the detection limit) and in the different controls carried out without algae (Table 1, Fig. 2). Significant increases of vaccenic acid photooxidation products were observed with algae amendment after 0.5 and 15H for low and high irradiance respectively (Wilcoxon test, n=6, p=0.031<0.05). As previously observed [START_REF] Rontani | On the origin of cis-vaccenic acid photodegradation products in the marine environment[END_REF][START_REF] Petit | Transfer of singlet oxygen from senescent irradiated phytoplankton cells to attached heterotrophic bacteria: Effect of silica and carbonaceous matrices[END_REF], photooxidation of heterotrophic bacteria membranes requires the presence of senescent phytoplankton cells. Our results showed a strong effect of intensity of irradiance on the photooxidation of the bacterial membranes (Table 1, Fig. 2). Indeed, the concentration of vaccenic acid photooxidation products reached 2741 ± 21 nmol.L -1 under high PAR irradiance but only 464 ± 190 nmol.L -1 under low irradiance (Fig. 2). The highest concentration of vaccenic acid oxidation products (3998 ± 1010 nmol.L -1 ) was observed under high UV+PAR irradiance (Table 1, Fig. 2) and may be attributed to the presence of additional UV radiations. In phytodetritus, chlorophyll can act as photosensitizer not only under PAR [START_REF] Nelson | Rates and possible mechanism of light-dependent degradation of pigments in detritus derived from phytoplankton[END_REF] but also under UV (Ying [START_REF] He | UV-B-induced formation of reactive oxygen species and oxidative damage of the cyanobacterium Anabaena sp.: protective effects of ascorbic acid and N-acetyl-L-cysteine[END_REF], since they absorb in the UV range (Queseda and Vincent, 1997;[START_REF] Ehling-Schulz | UV protection in cyanobacteria[END_REF]. This assumption is well supported by the profile of photooxidation products obtained under these conditions, showing the presence of a high proportion of allylic 11-and 12-hydroxyacids with a cis double bond (Fig. S1) typical of the UV-induced photooxidation of MUFAs [START_REF] Christodoulou | Comparative study of UV and visible light induced degradation of lipids in non-axenic senescent cells of Emiliania huxleyi[END_REF]. [START_REF] Amiraux | Paradoxical effects of temperature and solar irradiance on the photodegradation state of killed phytoplankton[END_REF] previously showed in senescent diatom cells that low irradiances induce a slow but long-lasting production of 1 O 2 that strongly oxidizes algal unsaturated lipids and sterols, through a process called "photodynamic effect" [START_REF] Knox | Singlet oxygen and plants[END_REF][START_REF] Skovsen | Lifetime and Diffusion of Singlet Oxygen in a Cell[END_REF], whereas high irradiances lead to stronger but shorter production of 1 O 2 that strongly photooxidizes the sensitizer (chlorophyll) through a process called "photobleaching" but with only a weak photodynamic effect [START_REF] Amiraux | Paradoxical effects of temperature and solar irradiance on the photodegradation state of killed phytoplankton[END_REF][START_REF] Rontani | Type II photosensitized oxidation in senescent microalgal cells at different latitudes: Does low under-ice irradiance in Polar Regions enhance efficiency?[END_REF]. This effect of the intensity of solar irradiance on the efficiency of the photodynamic effect in senescent phytoplankton cells was then confirmed in situ [START_REF] Rontani | Type II photosensitized oxidation in senescent microalgal cells at different latitudes: Does low under-ice irradiance in Polar Regions enhance efficiency?[END_REF].

The effect of irradiance on the efficiency of photooxidation of attached bacteria thus appears to be exactly the opposite of the effect on phytoplankton. Under low irradiance, the low flux of 1 O 2 produced reacts with the unsaturated membrane components of phytodetritus and thus gets quenched before it reaches attached bacteria (Fig. 3). In contrast, under strong irradiance, a higher proportion of 1 O 2 is efficiently transferred to the attached bacteria, strongly oxidizing their unsaturated lipids (Fig. 3). This new concept opens interesting perspectives in the study of phytoplankton-bacteria relationships.

Confirmation of the results obtained in vitro in samples collected in situ

To confirm the enhancement of the efficiency of photooxidation processes on bacteria attached to phytoplankton cells, we analyzed a variety (n=58) of sinking particulate matter and sea ice samples collected in the Arctic (Fig. 1), where irradiance at the sea ice-water interface and in the water column varies strongly according to the albedo of snow and ice and to snow and ice thickness (Chresten Lund-Hansen et al., 2021). Sinking particulate matter samples were collected: (i) in summer at 100 m in open water in the Beaufort Sea [START_REF] Rontani | Intense photooxidative degradation of planktonic and bacterial lipids in sinking particles collected with sediment traps across the Canadian Beaufort Shelf (Arctic Ocean)[END_REF], (ii) in summer at 25 m in the marginal ice zone of central Baffin Bay [START_REF] Rontani | Control of the preservation of sympagic algal material in surficial sediments of central and eastern Baffin Bay by bactericidal hydroperoxides and free fatty acids[END_REF], and (iii) in spring at 5 m and 30 m in ice-covered Resolute Passage [START_REF] Rontani | Monitoring abiotic degradation in sinking versus suspended Arctic sea ice algae during a spring ice melt using specific lipid oxidation tracers[END_REF]. Sea-ice samples (0-10 cm) were collected in spring during the GreenEdge campaign at Qikiqtarjuaq (Baffin Bay, Arctic Ocean; [START_REF] Amiraux | Stress factors resulting from the Arctic vernal sea-ice melt: Impact on the viability of bacterial communities associated with sympagic algae[END_REF]. To compare the efficiency of type-II photooxidation processes in algal and bacterial material, we used the ratio of % vaccenic acid photooxidation to % palmitoleic acid photooxidation. Vaccenic acid is generally considered to be specific to bacteria [START_REF] Lambert | Comparison of the effects of acid and base hydrolyses on hydroxy and cyclopropane fatty acids in bacteria[END_REF][START_REF] Sicre | Characterization of seawater samples using chemometric methods applied to biomarker fatty acids[END_REF], whereas palmitoleic acid is the main fatty acid component of sympagic diatoms [START_REF] Fahl | Lipid Content and fatty acid composition of algal communities in sea-ice and water from the Weddell Sea (Antarctica)[END_REF][START_REF] Falk-Petersen | Lipids and fatty acids in ice algae and phytoplankton from the Marginal Ice Zone in the Barents Sea[END_REF][START_REF] Leu | Increased irradiance reduces food quality of sea ice algae[END_REF]. Although palmitoleic acid is also present in several bacteria (e.g. de Carvalho and Caramujo, 2014), the contribution of bacteria to palmitoleic acid in the samples analyzed (dominated by diatoms) is negligible relative to the contribution of sympagic algae.

The results obtained show higher % vaccenic acid photooxidation/% palmitoleic photooxidation ratios in sinking particles collected in open-water Beaufort Sea than in icecovered Resolute Passage (Fig. 4A). This difference may be attributed to the mean PAR irradiance in the mixed surface layer, which is considerably higher in open water than in icecovered zones (e.g. 365 ± 62 and 10.9 ± 2.7 µmol photons.m -2 .s -1 during the spring-summer transition in Beaufort Sea, respectively; [START_REF] Alou-Font | Cell viability, pigments and photosynthetic performance of Arctic phytoplankton in contrasting ice-covered and open-water conditions during the spring-summer transition[END_REF]. The ratios observed in the Baffin Bay marginal ice zone (where ice concentration ranges from 15% to 80%) were logically intermediate (Fig. 4A). The decrease in the ratio observed between 5 and 30 m in the Resolute Passage (Fig. 4A) could be attributed to the presence in the deeper trap of highly aggregated senescent sea-ice algae settling sufficiently rapidly out of the euphotic zone to avoid significant photooxidation [START_REF] Rontani | Monitoring abiotic degradation in sinking versus suspended Arctic sea ice algae during a spring ice melt using specific lipid oxidation tracers[END_REF], however these differences were not significant (p-value > 0.05, see Fig. 4A, Table S1).

Very high ratios were found in the bottommost 10 cm of sea ice collected at the GreenEdge ice camp in 2016 (Fig. 4B). Under 75 cm of sea ice, irradiance can reach 105.9 µmol photons m -2 .s -1 without snow cover and decrease to 51.9 µmol photons m -2 .s -1 with a snow cover of 1 cm [START_REF] Lund-Hansen | Upwelling Irradiance below Sea Ice -PAR Intensities and Spectral Distributions[END_REF]. In this solid habitat, the bacteria-sympagic algae association may thus be maintained at relatively high irradiances during long periods, these conditions strongly favoring the photooxidation of bacteria. It was previously demonstrated that during the early stages of ice melt, sympagic bacteria undergo an intense osmotic stress in hypersaline ice brines [START_REF] Amiraux | Monitoring photo-oxidative and salinity-induced bacterial stress in the Canadian Arctic using specific lipid tracers[END_REF] and are affected later in the season by the release of bactericidal free palmitoleic acid by sympagic algae under the effect of light stress [START_REF] Amiraux | Stress factors resulting from the Arctic vernal sea-ice melt: Impact on the viability of bacterial communities associated with sympagic algae[END_REF]. Our results show that these organisms may also undergo a strong photooxidative stress in the ice resulting from a strong and efficient transfer of 1 O 2 from senescent sympagic algae. These different stresses are thought to strongly decrease the mineralization potential of bacteria attached to ice algae and thus favor the preservation of sympagic material in Arctic sediments [START_REF] Amiraux | Use of stress signals of their attached bacteria to monitor sympagic algae preservation in Canadian Arctic sediments[END_REF][START_REF] Rontani | Control of the preservation of sympagic algal material in surficial sediments of central and eastern Baffin Bay by bactericidal hydroperoxides and free fatty acids[END_REF].

4-Conclusion

Incubation of senescent cells of the diatom Thalassiosira sp. associated with the bacteria P. stutzeri under contrasting artificial light irradiances made it possible to demonstrate that high irradiances strongly favor photooxidation of the unsaturated membrane components of bacteria attached to senescent phytoplankton cells. Under these conditions, only a small part of 1 O 2 produced in chloroplasts reacts with membrane components of senescent algae, and so the remainder is efficiently transferred to their attached bacteria, inducing strong oxidative damage. Conversely, low irradiances favor photooxidation of the unsaturated algal lipids rather than attached bacteria. Indeed, 1 O 2 slowly generated under these conditions reacts with unsaturated lipids of the algal cells and is thus quenched before it can reach bacterial membranes. These findings were also confirmed in situ, where we observed the same tendencies in a variety of Arctic environmental samples (sea ice and sinking particulate matter). The transfer of 1 O 2 from senescent algae to their attached bacteria appeared to be particularly efficient in sea ice where the sympagic algae-bacteria association is maintained at relatively high irradiances during long periods of time. showing the production of significant proportions of 12-hydroxyoctadec-10(cis)-enoic and 11-hydroxyoctadec-12(cis)-enoic acids (indicative of an effect of UV radiation, [START_REF] Christodoulou | Comparative study of UV and visible light induced degradation of lipids in non-axenic senescent cells of Emiliania huxleyi[END_REF], respectively, after irradiation of Thalassiosira sp. cells associated with P. stutzeri under high solar irradiance (500 W.m -2 ) for 3h 30. ) 0 ( w i t h o u t a l g a e )

1 8 ( P A R ) 1 8 ( P A R ) ( w i t h o u t a l g a e ) 4 4 6 ( P A R ) 4 4 6 ( P A R ) ( w i t h o u t a l g a e ) 5 0 0 ( P A R + U V ) 5 0 0 ( P A R + U V ) ( w i t h o u t a l g a e )

  FIGURE CAPTIONS

  products (pmol.L -1

Table 1 .

 1 Concentration of vaccenic acid photooxidation products in P. stutzeri cultures with and without addition of Thalassiosira sp. cells after irradiation (1000 KJ.m -2 ) under low PAR, high PAR and high PAR+UV irradiances (n=3).

	Irradiance (W m -2 )	Vaccenic acid hv (pmol.L -1 ) a	Standard deviation
	0 (without algae)	24	6
	18 (PAR)	464	190
	18 (PAR) (without algae)	13	0
	446 (PAR)	2741	21
	446 (PAR) (without algae)	27	3
	500 (PAR + UV)	3998	1010
	500 (PAR + UV) (without algae)	41	4

a Mean values.

Journal of Phycology

Acknowledgements

Sediment trap samples from the Beaufort Sea were obtained through the Long-Term Oceanic Observatories Program led by ArcticNet-Network of Centers of Excellence (NCE)

of Canada. The collection of sinking particles from Resolute Passage was supported through a University of Manitoba start-up grant and the Natural Sciences and Engineering Research Council of Canada (NSERC). Sinking particles and sea ice samples from Baffin Bay were collected in the framework of the GreenEdge project funded by the following French and Canadian programs and agencies: ANR (Contract #111112), CNES (project #131425), IPEV (project #1164), CSA, Fondation Total, ArcticNet, LEFE, and the French Arctic Initiative. The project was conducted with scientific coordination by the Canada Excellence Research Chair on Remote sensing of Canada's New Arctic Frontier and the CNRS/Université Laval Takuvik joint international laboratory (UMI3376). We thank Québec-Ocean, the CCGS Amundsen, and the Polar Continental Shelf Program for their in-kind contribution to polar logistics and scientific equipment, and the European Regional Development Fund (ERDF) project Oceanomed (No. 1166-39417) for funding the GC-MS/MS system employed. Thanks are also due to two anonymous reviewers for their very useful and constructive comments.

Table S1: Significant differences between % vaccenic acid photooxidation to % palmitoleic acid photooxidation ratios measured in the Arctic under different irradiances n = 3,KW test)