class: center, middle, inverse, title-slide # Nitrapyrin effects on soil microbial community structure, composition, diversity & function ### Ruth Schmidt*, Xiao-Bo Wang, Paolina Garbeva and Étienne Yergeau ### *Mitacs Postdoctoral Fellow (INRS, Plotly) ### 2020/06/22 --- <style type="text/css"> .tiny .remark-code { /*Change made here*/ font-size: 50% !important; } th { font-size: 12px; } td { font-size: 12px; } # b, strong { # color: black; # } .remark-slide tr:nth-child(2n) { background-color: white !important; } </style> ## N in agriculture & recap of N-cycle <img width="400"; img style="float: right;" src="images/ni_agriculture.png"> - Nitrogen (N) most limiting nutrient for plant growth - To counteract this problem, N-containing fertilizers are commonly applied - Negative effects: atmospheric and groundwater pollution through nitrate leaching and volatilization - Linked to soil microbial processes (nitrification and denitrification) <br/><br/> <br/><br/> <br/><br/> <font size="1"> Figure source: Nitrification in agricultural soils: impact, actors and mitigation <a href="https://www-sciencedirect-com.erable.inrs.ca:2048/science/article/pii/S0958166917302239#fig0005">(Beeckmann et al., 2018) </a></font> --- ## Nitrification & inhibitors - First step of nitrification carried out by ammonia oxidizing bacteria and archaea (AOB & AOA) - Keystone species* ubiquitously found in soil, AOA more abundant than AOB - Nitrification inhibitors (e.g. Nitrapyrin) commonly used in combination with N-fertilizers to counteract adverse effects while increasing soil N retention and crop yields <p align="center"> <img width="400" src="images/nitrification.png"> </p> <font size="1">*A keystone species is a species which has a disproportionately large effect on its natural environment relative to its abundance (source: Wikipedia)</font> --- ## Nitrapyrin mode of action - One of the most common nitrification inhibitors - Delays nitrification by temporarily deactivating of ammonia monooxygenase (AMO) - enzyme responsible for ammonia oxidation - α-subunit of AMO is encoded by the amoA gene, which is homologous in AOA and AOB - Both AOA and AOB could be inhibited by nitrapyrin <p align="center"> <img width="700" src="images/nitrapyrin.png"> </p> <font size="1"> Figure source: Nitrification in agricultural soils: impact, actors and mitigation <a href="https://www-sciencedirect-com.erable.inrs.ca:2048/science/article/pii/S0958166917302239#fig0005">(Beeckmann et al., 2018) </a></font> --- ### What are the effects of nitrapyrin on AOA and AOB? - Studies found varying effects on AOA and AOB gene abundance (*amoA* gene), AOA:AOB ratio and nitrification rates ([<strong style="color: #66C2A5;">Lehtovirta-Morley et al., 2013</strong>](https://www.nature.com/articles/ismej201542), [<strong style="color: #66C2A5;">Shen et al., 2013</strong>](https://academic.oup.com/femsle/article-lookup/doi/10.1111/1574-6968.12164), [<strong style="color: #66C2A5;">Gu et al., 2018</strong>](https://www.researchgate.net/publication/326259363_Nitrapyrin_affects_the_abundance_of_ammonia_oxidizers_rather_than_community_structure_in_a_yellow_clay_paddy_soil)) - Suggest that nitrapyrin shifts the AOA:AOB ratio, thus inhibiting nitrification at different rates depending on contribution of AOA and AOB to nitrification - BUT: not known how nitrapyrin affects overall microbial community structure, composition, diversity and functions, and how this varies through the growing season --- ### Nitrapyrin & volatile organic compounds (VOCs) - VOCs are small compounds (up to C20) with low molecular mass (100–500 Daltons), high vapour pressure, low boiling point and a lipophilic moiety ([<strong style="color: #66C2A5;">Schmidt et al., 2015</strong>](https://www.nature.com/articles/ismej201542)) - Key metabolites in below-ground microbe and plant-microbe interactions & influence important biogeochemical processes (e.g. N-cycle) ([<strong style="color: #66C2A5;">Schmidt et al., 2019</strong>](https://www.nature.com/articles/s41396-019-0469-x?proof=trueSouthampton), [<strong style="color: #66C2A5;">Schulz-Bohm et al., 2017</strong>](https://www.frontiersin.org/articles/10.3389/fmicb.2017.02484/full), [<strong style="color: #66C2A5;">De la Porte et al., 2020</strong>](https://linkinghub.elsevier.com/retrieve/pii/S0966842X20300524)) - Some VOCs (especially monoterpenes) inhibit nitrification (target: AMO) <p align="center"> <img width="600" src="images/voc.png"> </p> <font size="2"> Patterns of microbial communication across terrestrial ecosystems (Schmidt et al., 2019)</font> --- ## Hypothesis & Objectives <strong style="color: #66C2A5;">Hypothesis</strong>: Nitrapyrin-induced shifts in the abundance of the keystone groups AOA and AOB affect the overall microbial community structure, composition, diversity and functions <strong style="color: #66C2A5;">Objectives</strong>: Explore the effects of nitrapyrin applied on field-grown wheat on - overall microbial community structure, composition and diversity - abundance of genes encoding for enzymes involved in ammonia oxidation, N-fixation, and denitrification - microbial VOC emission in the rhizopshere --- ## A field study <strong style="color: #66C2A5;">Design & setup</strong>: Random block design (6 replicates), wheat seeds (AC Walton) sown in 4 rows on each plot Fertilizer liquid ammonium nitrate (NH₄NO₃) & nitrification inhibitor nitrapyrin (NI) applied twice during the growing season <p align="center"> <img width="430" src="images/setup.png"> </p> --- ## Data overview <strong style="color: #66C2A5;">Treatment</strong>: - Control (without application of fertilizer + NI) - NI (with application of fertilizer + NI) <strong style="color: #66C2A5;">Sampling date</strong>: - 2019-07-23 = grain-filling period - 2019-09-05 = harvest <strong style="color: #66C2A5;">Compartment</strong>: - Bulk soil - Rhizosphere soil --- class: inverse, middle, center # Diversity, composition and structure of soil microbial communities --- ### Alpha diversity not affected by NI treatment, but by Date & Compartment Three-way repeated measures ANOVA of bacterial, archaeal (16S) and fungal (ITS) alpha diversity examined by Shannon index, Inverse Simpson and Faith’s PD | | 16S | | | | | | ITS | | | | | | |--------------------------------------- |---------------- |-------------- |------------------------ |-------------- |-------------- |-------------- |---------------- |---------------- |------------------------ |---------------- |--------------- |---------------- | | | Shannon | | Inverse Simpson | | PD | | Shannon | | Inverse Simpson | | PD | | | | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | | Treatment | 0.001 | 0.971 | 0.555 | 0.461 | 1.741 | 0.195 | 0.588 | 0.448 | 0.658 | 0.422 | 0.924 | 0.342 | | Compartment | 0.281 | 0.599 | 0.612 | 0.439 | 7.977 | **0.007** | 9.523 | **0.004** | 17.086 | **<0.001** | 0.308 | 0.582 | | Date | 1.175 | 0.285 | 0.016 | **0.003** | 0.378 | 0.542 | 49.577 | **<0.001** | 53.389 | **<0.001** | 26.062 | **<0.001** | | Treatment × Compartment | 0.02 | 0.888 | 2.838 | 0.1 | 0.121 | 0.73 | 1.314 | 0.258 | 2.237 | 0.143 | 0.74 | 0.395 | | Treatment × Date | 0.978 | 0.329 | 0 | 0.997 | 0.923 | 0.342 | 1.492 | 0.229 | 0.905 | 0.347 | 0.21 | 0.649 | | Compartment × Date | 1.552 | 0.22 | 1.273 | 0.266 | 2.485 | 0.123 | 0 | 0.989 | 2.586 | 0.116 | 1.434 | 0.238 | | Treatment × Compartment × Date | 0.082 | 0.777 | 1.714 | 0.198 | 0.345 | 0.560 | 0.119 | 0.732 | 1.948 | 0.171 | 0.416 | 0.522 | --- class: inverse, middle, center # Time for some interactive graphs! --- ### Increased diversity at the second date (Inv Simpson) & lower diversity in bulk soil (PD) Bacterial & archaeal alpha diversity <div id="htmlwidget-fc7b4e26279696571dc3" style="width:792px;height:432px;" class="plotly html-widget"></div> <script type="application/json" 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--- ### Lower diversity in rhizosphere (Shannon & Inv Simpson), and second date (Shannon, Inv Simpson & PD) Fungal alpha diversity <div id="htmlwidget-29297dd91360ddde55a5" style="width:792px;height:432px;" class="plotly html-widget"></div> <script type="application/json" 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--- ### Stacked bar charts are not that boring after all (let's have a look at Thaumarchaeota) Bacterial and archaeal communities (mean relative abundance >1%) <div id="htmlwidget-32bac940ced7f937fdd0" style="width:792px;height:432px;" class="plotly html-widget"></div> <script type="application/json" 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--- ### NI treatment affected relative abundance of archaeal phylum Thaumarchaeota, Date & Compartment affected several Phyla Three-way repeated measures ANOVA of relative abundance of dominant phyla of bacterial & archaeal (16S) communities | 16S Phyla | T | | C | | D | | T x C | | T x D | | C x D | | T x C x D | | |--------------------------- |-------------- |-------------- |--------------- |------------------ |--------------- |------------------ |-------------- |-------------- |-------------- |-------------- |--------------- |------------------ |------------------ |-------------- | | | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | | **Thaumarchaeota** | 4.463 | **0.041** | 11.899 | **0.001** | 23.796 | **<0.001** | 5.845 | **0.02** | 0.282 | 0.598 | 5.301 | **0.027** | 3.873 | 0.056 | | Verrucomicrobia | 0.216 | 0.645 | 3.353 | 0.075 | 42.408 | **<0.001** | 1.456 | 0.235 | 2.848 | 0.099 | 0.341 | 0.563 | 1.353 | 0.252 | | **Proteobacteria** | 2.233 | 0.143 | 6.288 | **0.016** | 2.218 | 0.144 | 4.912 | **0.032** | 0.917 | 0.344 | 0.047 | 0.829 | 3.643 | 0.064 | | Actinobacteria | 0.571 | 0.454 | 1.162 | 0.288 | 0.019 | 0.89 | 0.581 | 0.45 | 0.292 | 0.592 | 17.476 | **<0.001** | 2.934 | 0.094 | | **Nitrospirae** | 0.041 | 0.841 | 17.094 | **<0.001** | 0.067 | 0.797 | 0.353 | 0.556 | 4.91 | **0.032** | 1.626 | 0.21 | 0.751 | 0.391 | | Acidobacteria | 0.314 | 0.578 | 4.703 | **0.036** | 50.902 | **<0.001** | 0.095 | 0.759 | 0.838 | 0.366 | 1.952 | 0.17 | 0.001 | 0.979 | | Firmicutes | 0.828 | 0.368 | 10.89 | **0.002** | 75.806 | **<0.001** | 2.044 | 0.161 | 1.378 | 0.247 | 19.477 | **<0.001** | 1.766 | 0.191 | | Bacteroidetes | 1.346 | 0.253 | 2.812 | 0.101 | 0.548 | 0.463 | 2.205 | 0.145 | 1.075 | 0.306 | 0.127 | 0.723 | 1.889 | 0.177 | | Gemmatimonadetes | 1.605 | 0.213 | 0.897 | 0.349 | 10.954 | **0.002** | 0.323 | 0.573 | 0.386 | 0.538 | 1.292 | 0.262 | 0.817 | 0.371 | --- ### NI treatment increased Thaumarchaeota abundance 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--- ### NI treatment affected relative abundance of archaeal genus *Nitrososphaera* Three-way repeated measures ANOVA of relative abundance of dominant genera of bacterial & archaeal (16S) communities | 16S Genera | T | | C | | D | | T x C | | T x D | | C x D | | T x C x D | | |------------------------------- |-------------- |------------------ |--------------- |------------------ |--------------- |------------------- |-------------- |------------------ |-------------- |-------------- |--------------- |------------------- |------------------ |------------------ | | | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | | *Nitrososphaera* | 4.463 | **0.041** | 11.899 | **0.001** | 23.796 | **<0.001** | 5.845 | **0.02** | 0.282 | 0.598 | 5.301 | **0.027** | 3.873 | 0.056 | | *Gaiella* | 0.46 | 0.501 | 0.022 | 0.883 | 2.026 | 0.162 | 4.697 | **0.036** | 0.757 | 0.39 | 3.386 | 0.073 | 8.954 | **0.005** | | *Gp6* | 0.345 | 0.561 | 6.855 | **0.012** | 40.88 | **<0.001** | 0.004 | 0.95 | 2.253 | 0.141 | 2.912 | 0.096 | 0.131 | 0.719 | | *Solirubrobacter* | 0.468 | 0.498 | 2.183 | 0.147 | 0.016 | 0.899 | 0.863 | 0.358 | 0.202 | 0.656 | 20.462 | **<0.001** | 4.491 | **0.04** | | *Gp16* | 0.067 | 0.797 | 2.407 | 0.129 | 26.357 | **<0.001** | 3.064 | 0.088 | 0.067 | 0.797 | 0.017 | 0.897 | 3.673 | 0.062 | | *Hyphomicrobium* | 0.113 | 0.738 | 43.124 | **<0.001** | 23.043 | **<0.001** | 1.307 | 0.26 | 3.194 | 0.081 | 40.601 | **<0.001** | 5.7 | **0.022** | | *Rhodoplanes* | 2.332 | 0.135 | 11.46 | **0.002** | 0.956 | 0.334 | 4.643 | **0.037** | 0.069 | 0.794 | 15.071 | **<0.001** | 1.961 | 0.169 | | *Spartobacteria (gis)* | 0.648 | 0.425 | 0.667 | 0.419 | 64.727 | **<0.001** | 4.032 | 0.051 | 2.008 | 0.164 | 1.872 | 0.179 | 3.035 | 0.089 | | *Povalibacter* | 0.892 | 0.351 | 0.115 | 0.736 | 7.337 | **0.01** | 0.436 | 0.513 | 0.054 | 0.817 | 0.001 | 0.974 | 0.697 | 0.409 | | *Sphingomonas* | 0.043 | 0.836 | 9.123 | **0.004** | 1.466 | 0.233 | 3.431 | 0.071 | 0.145 | 0.706 | 2.996 | 0.091 | 4.016 | 0.052 | --- ### Interaction effect of NI treatment & Compartment affected relative abundance of fungal phylum Basidiomycota Three-way repeated measures ANOVA of relative abundance of dominant phyla of fungal (ITS) communities | ITS Phyla | T | | C | | D | | T x C | | T x D | | C x D | | T x C x D | | |--------------------------- |-------------- |-------------- |-------------- |-------------- |--------------- |------------------- |-------------- |----------------- |-------------- |-------------- |-------------- |------------------ |------------------ |-------------- | | | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | *F* | *P* | | Mortierellomycota | 0.037 | 0.849 | 1.505 | 0.227 | 12.566 | **0.001** | 1.561 | 0.219 | 2.924 | 0.095 | 7.27 | **0.01** | 2.407 | 0.129 | | Ascomycota | 0.081 | 0.777 | 0.897 | 0.349 | 17.854 | **<0.001** | 2.676 | 0.11 | 4.004 | 0.052 | 7.177 | **0.011** | 2.161 | 0.149 | | 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--- ### Not NI treatment, but Compartment & Date altered overall structure of bacterial and archaeal & fungal communities PERMANOVA of bacterial, archaeal (16S) and fungal (ITS) community structure | | 16S | | ITS | | |--------------------------------------- |-------------- |-------------- |-------------- |-------------- | | | *R²* | *P* | *R²* | *P* | | Treatment | 0.019 | 0.244 | 0.016 | 0.422 | | Compartment | 0.05 | **0.001** | 0.035 | **0.020** | | Date | 0.124 | **0.001** | 0.187 | **0.001** | | Treatment × Compartment | 0.041 | **0.004** | 0.031 | **0.048** | | Treatment × Date | 0.015 | 0.464 | 0.016 | 0.408 | | Date × Compartment | 0.057 | **0.002** | 0.033 | **0.038** | | Treatment × Date × Compartment | 0.036 | **0.019** | 0.029 | 0.076 | --- ### Date separated beteween rhizosphere and bulk soil (16S) Principal Coordinates Analysis (PCoA) of the bacterial & archaeal (16S) community composition based on Bray-Curtis dissimilarity <p align="center"> <img width="700" src="images/pcoa_16s.png"> </p> --- ### Date separated beteween rhizosphere and bulk soil (ITS) Principal Coordinates Analysis (PCoA) of the fungal (ITS) community composition based on Bray-Curtis dissimilarity <p align="center"> <img width="700" src="images/pcoa_its.png"> </p> --- ### Summary (16S&ITS results) - Nitrapyrin treatment, among strong effect of compartment and sampling date, affected relative abundance of many bacterial, archaeal and fungal taxa (e.g. Proteobacteria, Nitrospirae, Basidiomycota) - Proteobacteria contain AOB - Nitrospirae (nitrite-oxidizing bacteria often in close association with AOB or AOA) - Basidiomycota contain denitrifiers that play key roles in N cycle - Nitrapyrin had significant effects on expected targets, ammonia-oxidizers - AOA expected to increase and AOB decrease due to stronger inhibition of AOB - AOA *Nitrososphaera* and Thaumarcheota increased - BUT: no observed effect for AOB (below detection limit) --- class: inverse, middle, center # Abundance of functional genes of the N cycle --- ### Abundances of the N-cycling genes not affected by NI treatment, but strongly affected by Date Kruskal-Wallis test of abundance of functional genes involved in N cycle | | T | | C | | D | | T x C | | T x D | | C x D | | T x C x D | | |---------------------- |-------------- |-------------- |-------------- |-------------- |--------------- |---------------- |-------------- |-------------- |--------------- |---------------- |--------------- |---------------- |------------------ |---------------- | | | χ² | *P* | χ² | *P* | χ² | *P* | χ² | *P* | χ² | *P* | χ² | *P* | χ² | *P* | | amoA-AOA | 1.021 | 0.312 | 1.150 | 0.284 | 21.716 | **<0.001** | 2.205 | 0.531 | 22.741 | **<0.001** | 25.724 | **<0.001** | 27.049 | **<0.001** | | amoA-AOB | 0.435 | 0.509 | 0.17 | 0.68 | 22.491 | **<0.001** | 2.24 | 0.524 | 22.954 | **<0.001** | 28.582 | **<0.001** | 30.816 | **<0.001** | | AOA:AOB ratio | 0.188 | 0.665 | 0.043 | 0.837 | 27.215 | **<0.001** | 1.71 | 0.635 | 27.445 | **<0.001** | 32.125 | **<0.001** | 28.16 | **<0.001** | | nirK | 0.002 | 0.962 | 1.728 | 0.189 | 32 | **<0.001** | 1.774 | 0.621 | 32.683 | **<0.001** | 34.569 | **<0.001** | 35.624 | **<0.001** | | nirS | 0.051 | 0.821 | 2.521 | 0.112 | 14.551 | **<0.001** | 4.314 | 0.23 | 19.38 | **<0.001** | 21.324 | **<0.001** | 28.16 | **<0.001** | | nifH | 1.734 | 0.188 | 0.322 | 0.57 | 32.87 | **<0.001** | 2.121 | 0.548 | 36.287 | **<0.001** | 33.454 | **<0.001** | 37.265 | **<0.001** | --- ### Abundance of *amoA*-AOA, *nirK*, *nifH* & *nirS* increased, while *amoA*-AOB decreased over time <p align="center"> <img width="700" src="images/qpcr_all.png"> </p> --- ## Summary (qPCR results) - Shift (increase) in relative abundance of AOA phyla and genera confirmed by qPCR of archaeal *amoA* gene (dependent on sampling date) - Similar trends (decrease) observed for AOB *amoA* gene abundance in rhizosphere, suggesting weak efficiency of nitrapyrin on target group - AOA:AOB ratios >1 (soils low in AOB) - Sampling date was overarching factor affecting abundance of all N-cycle genes, with stronger effects on first sampling date (limited residence time in soil) --- class: inverse, middle, center # Volatile organic compound (VOC) profiles --- ### Before we dive into the results, let's have a glimpse at the metabolomics data Metabolomics data structure after normalization .tiny[ ```r # glimpse() is like transposed version of print() # data.table() is an enhanced version of read.table (detects sep, colClasses and nrows automatically), e.g. fread() library(data.table) mset = data.frame(fread("/Users/ruthschmidt/Dropbox/Work/INRS/Data/NI_experiment/Files/VOC/data_normalized.csv", header=T)) glimpse(mset) ``` ``` ## Rows: 397 ## Columns: 25 ## $ ID <chr> "2:39.0239@4.34 (2-Picoline, 6-nitro-)", "5:41.0393@2.65 (Acetic… ## $ C1_1 <dbl> 0.99612847, 1.17741294, 1.30116310, 0.11315459, 0.58418700, 1.14… ## $ C1_2 <dbl> -0.98692061, -0.82511936, 0.89388240, 0.67710410, 0.91347540, -1… ## $ C2_1 <dbl> 0.77056605, 1.26379973, -1.01594020, 0.12086544, 0.49139500, 1.2… ## $ C2_2 <dbl> -1.03844604, -0.75240572, 0.59400100, 0.46031433, 0.79126180, 0.… ## $ C3_1 <dbl> 1.0072837, 0.5954468, 1.7478495, 0.6275328, -1.1834992, 0.870440… ## $ C3_2 <dbl> 0.70390366, -0.11760454, -1.00852080, 0.44338935, 0.78026210, 0.… ## $ C4_1 <dbl> -0.305153220, 0.171339670, -1.040703100, 0.523495940, -1.2970187… ## $ C4_2 <dbl> 0.20962962, -0.96142020, 0.60655030, 0.54596879, 0.53573230, -1.… ## $ C5_1 <dbl> 0.297530520, 0.573360510, -1.042560900, -1.927893540, -1.2993800… ## $ C5_2 <dbl> -0.168318480, -0.687110460, 1.372546400, 0.800043910, 0.91606890… ## $ C6_1 <dbl> 0.8610605, 1.3230990, -1.0071706, -1.8775755, -1.2543994, 1.1651… ## $ C6_2 <dbl> -1.14258605, -0.61457896, 0.61751670, 0.63317136, 0.81936700, -1… ## $ N1_1 <dbl> 0.8627581, 0.9203053, -0.9945753, -1.8596675, -1.2383910, 0.9880… ## $ N1_2 <dbl> 0.65735836, 1.26171425, -1.15217180, 0.61546751, -1.43869380, -1… ## $ N2_1 <dbl> 1.2508663, 1.1869419, -0.9030305, 0.5024116, 0.7001255, 0.743304… ## $ N2_2 <dbl> -1.88570010, -1.59109300, 0.55924830, 0.19878731, 0.74244550, -1… ## $ N3_1 <dbl> 1.24246639, 1.19845886, -0.92040620, -1.75421373, -1.14412310, 1… ## $ N3_2 <dbl> -1.18474878, -1.04092505, 0.36092020, 0.58820241, 0.93040390, 0.… ## $ N4_1 <dbl> -0.01077812, -0.08877474, 0.81224630, 0.29850596, -1.09446820, 0… ## $ N4_2 <dbl> -0.04228815, -0.96642186, -1.03139870, 0.73180790, 0.95307030, -… ## $ N5_1 <dbl> 0.41268699, 0.46180647, 0.90301740, 0.07013754, 0.38094690, 0.60… ## $ N5_2 <dbl> -0.50972304, -0.83613079, 0.65094450, 0.77991340, 0.91012510, -1… ## $ N6_1 <dbl> 0.3938863, 0.2694553, 0.7975968, -1.9218008, -1.2939336, 0.70049… ## $ N6_2 <dbl> -2.39146230, -1.92155613, -1.10100500, 0.61087692, 0.79504020, -… ``` ] --- ### VOC profiles not affected by NI treatment, but differed among Date .pull-left[ Principal Component Analysis (PCA) of rhizosphere VOC composition <div 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] .pull-right[ PERMANOVA of rhizosphere VOC composition | | *R^2* | *P* | |------------------------- |------------------------ |----------------- | | Treatment | 0.013 | 0.384 | | Date | 0.665 | **0.001** | | Treatment × Date | 0.016 | 0.304 | ] --- ### Monoterpene α-Pinene most significant compound .tiny[ ```r # DT(): R data objects (matrices or data frames) can be displayed as tables on HTML pages, and DataTables provides filtering, # pagination, sorting, and many other features in the tables library(DT) voc_table <- fread(file="images/voc_table.txt") DT::datatable( voc_table, rownames = FALSE, extensions = 'FixedColumns', fillContainer = FALSE, options = list(pageLength = 8)) ``` <div id="htmlwidget-40cc8d303d06f148c9bc" style="width:100%;height:auto;" class="datatables html-widget"></div> <script type="application/json" data-for="htmlwidget-40cc8d303d06f148c9bc">{"x":{"filter":"none","extensions":["FixedColumns"],"fillContainer":false,"data":[["1377_alpha-Pinene","386_Hexanoic 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--- ## Summary (VOC results) - Contrary to expectations, we did not find significant direct effect of nitrapyrin treatment on VOC profiles (sampling time) - But VOC profiles clearly differed according to sampling date - α-Pinene (monoterpene) previously shown to inhibit nitrification by targeting the ammonia monooxygenase enzyme - linked to large decrease in AOB *amoA* abundance at the second sampling date? - AOB might be more susceptible to inhibitory effects of alpha-pinene than AOA <p style="text-align:center;"><img width="150"; src="images/pinene.png" alt="Logo"></p> --- ## Conclusions - Results confirm hypothesis that nitrapyrin alters the relative abundance of non-target bacteria, archaea, fungi and associated nitrogen-cycle functions - Effects of nitrification inhibitors might be far reaching, but difficult to determine whether shifts caused directly (e.g. off-target toxic effects) or indirectly (through reduced activities of ammonia-oxidizers) - Shifts not reflected in VOC profiles of rhizosphere, suggesting that nitrapyrin did not affect soil functionality - Nitrapyrin affected abundance of AOA, but positively - Nitrapyrin effects also constrained by sampling date and by the plant compartment, suggesting interaction with environmental conditions --- class: center ### A HUGE THANKS TO ALL OF YOU! <br></br> <br></br> <img width="800"; img src="images/field.png"> --- ### Some useful recources <font size="4"> Courses I took: - [<strong style="color: #66C2A5;">Cleaning data in R</strong>](https://learn.datacamp.com/courses/cleaning-data-in-r) (Datacamp) - Udemy data science courses (R, Python) (regular offers) - [<strong style="color: #66C2A5;">Coursera data science courses</strong>](https://www.coursera.org/specializations/jhu-data-science?aid=true#courses) (audit for free) Papers I read: - [<strong style="color: #66C2A5;">Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible</strong>](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003531) - [<strong style="color: #66C2A5;">Rarefaction, Alpha Diversity, and Statistics</strong>](https://www.frontiersin.org/articles/10.3389/fmicb.2019.02407/full) - [<strong style="color: #66C2A5;">Normalization and microbial differential abundance strategies depend upon data characteristics</strong>](https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-017-0237-y) Documentations: - Markdown (Presentation R package [<strong style="color: #66C2A5;">xaringan</strong>](https://github.com/yihui/xaringan), [<strong style="color: #66C2A5;">Tutorial</strong>](https://www.markdowntutorial.com/), [<strong style="color: #66C2A5;">Tutorial</strong>Cheat Sheet</strong>](https://rstudio.com/wp-content/uploads/2016/03/rmarkdown-cheatsheet-2.0.pdf?_ga=2.141878454.584902075.1592179904-1367643102.15861999880)) - [<strong style="color: #66C2A5;">Interactive web-based data visualization with R, plotly, and shiny</strong>](https://plotly-r.com/index.html) - [<strong style="color: #66C2A5;">Plotly (R)</strong>](https://plotly.com/r/) R packages: - [<strong style="color: #66C2A5;">phyloseq</strong>](https://joey711.github.io/phyloseq/), microbiome, microbiomeutilities, [<strong style="color: #66C2A5;">heatmaply</strong>](https://cran.r-project.org/web/packages/heatmaply/vignettes/heatmaply.html), tidyr, dplyr (part of [<strong style="color: #66C2A5;">tidyverse</strong>](https://www.tidyverse.org/)), [<strong style="color: #66C2A5;">data.table</strong>](https://cran.r-project.org/web/packages/data.table/vignettes/datatable-intro.html) Ask me! --- class: center ### But most importantly, don't give up! ... even if the road is difficult <img width="450"; img src="images/road.jpg">