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1
Electric Supplementary Material
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Statistical recipe for quantifying microbial functional diversity from EcoPlate metabolic
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profiling
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Takeshi Miki, Taichi Yokokawa, Po-Ju Ke, I Fang Hsieh, Chih-hao Hsieh, Tomonori Kume,
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Kinuyo Yoneya, Kazuaki Matsui
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[email protected]
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Additional methods A: Detailed methods in soil samples
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In December 2012, we selected three 1 m × 1 m subplots along a 400-m2 plot and created
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trenches (depth: 50–70 cm, width: 40 cm) around the three plots using a shovel. Trenches
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prevented other live roots and rhizomes from extending into the trenching plots. Understory
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vegetation in the trenching plots was pulled out before every measurement, to minimize effects
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of root activity of the understory vegetation. More information is available in Lin et al.
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(submitted to Ecological Research). We separated 5-g subsamples from each sieved sample soil
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and prepared a 1:1000 dilution with 50 μM NaHPO3 solution. We added 100 μL of the diluted
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sample to each well of the EcoPlate, and incubated plates under the same temperature as that of
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the sampling condition.
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Additional methods B: Summary of R script (ecopl_comparison_20170614.R) for main
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analyses
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Preparation of environment
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First, R (https://www.r-project.org/), R-studio (https://www.rstudio.com/products/RStudio/), and
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Java (https://java.com/en/) (Java SE Runtime Environment) are installed. Chemistry
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Development
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downloaded from https://sourceforge.net/projects/cdk/files/cdk/ and installed according to the
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instructions at http://cdk.sourceforge.net/old_web/download.html. Java environment and CDK
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can be installed to use the chemoinformatic tool (rcdk library) in R. As installation steps for Java
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and CDK are more complicated in Mac OSX (9.5 or later) than in Windows and Linux, readers
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are directed to guidance_OSX.pdf for more information. In the event that installation of the CDK
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library fails for any operating system (OS), some parts of the R script can be skipped and results
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from the chemoinformatic tool can be directly loaded (from line 69 in this R script) without
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conducting the analysis from the sdf files. When executing the R script in the R-studio
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environment, the Working Directory should be set from the toolbar as follows: Session > Set
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Working Directory > To Source File Location. All of the functions in this script
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(ecopl_comparison_20170614.R) rely on the relative path from this R script (i.e., Source)
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location for making access to other files (e.g., EcoPlate data and chemical information). Each
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part of the script is described in the following subsections (PART0 to PART4).
Kit
(CDK)
(ref:
http://pubs.acs.org/doi/abs/10.1021/ci025584y)
is
then
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PART0: Setting environment
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Libraries used for the analyses can be installed by the scripts in this part. Alternatively, each
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library except for pforeach can be installed from the toolbar of R-studio. Readers who are
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unfamiliar with multivariate analysis in ecology should read Appendix C first and then continue
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to the following parts.
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PART1: Analysis of chemical similarity
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By executing the script in this part, the chemical dissimilarity matrix (DC) and similarity trees
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(TC) for the 31 substrates in EcoPlate shown in Fig. 4 are obtained. This part totally relies on the
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library {rcdk}. Any error messages that result from loading this library (library(“rcdk”)) are
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likely due to incompatibility of versions of the Java environment and/or CDK in the OS. These
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programs should be updated and the environments rebooted. In the R environment, because the
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library rcdk depends on a specific version of another library rJava, and because the installation
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processes of rcdk include installation of the version of rJAVA, rJava should not be independently
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installed or updated. One can also refer to guidance_OSX.pdf.
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PART2: EcoPlate data loading for EcoPlate analysis
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This part includes the functions to load EcoPlate data and loading processes of all data. As the
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environment of data accumulation (e.g., file name) is highly variable, the functions to load
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should be adjusted depending on the project. More detailed notes are available in the beginning
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of PART2 in this script.
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PART3: EcoPlate analysis combined with chemical similarity
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This part of the script includes the i) definitions of functions, ii) analyses for the microcosm
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experiment, and iii) analyses for the Xitou data.
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PART4: Additional analysis for SI
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This part of the script is composed of i) checking the distribution of statistical values from
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randomly generated similarity trees (for Fig. S6), ii) comparing the chemical dissimilarity matrix
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and in situ color development dissimilarity matrix and generating the trees shown in Fig. S5, iii)
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subanalysis to investigate the effect of integration period on the explanatory power of the
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statistical model for specific datasets (the best datasets) (for Fig. S4), and iv) scripts for
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comparing PERMANOVA and distance-based RDA.
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Additional methods C: Basis for data prearrangement
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The processes in Fig. 3 are standard procedure for multivariate analysis in various fields in
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ecology and environmental science. Readers who are unfamiliar with these processes should
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review the example in ecopl_pre_confirmation.R. Conversion from the functional matrix EC into
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EBC, MF, and DF (DFBUW, DFUW) can be confirmed by this R script, and identical results are
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available in sample_pre_data.xlsx.
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Additional methods D: Supplementary methods
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Chemoinformatic analyses
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We complied two-dimensional structures of the 31 carbon substrates used in EcoPlate into a
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single sdf file (list_ecoplate2.sdf). The item with “ FDB***” was
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downloaded from FooDB (http://foodb.ca/); all others were downloaded from PubChem
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(https://pubchem.ncbi.nlm.nih.gov/)
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ftp://ftp.ncbi.nlm.nih.gov/pubchem/specifications/pubchem_fingerprints.pdf for the definition of
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fingerprint format). This file was used to generate chemical similarity trees tree-a and tree-b
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through
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(http://chemminetools.ucr.edu/tools/view_job/118888/) was used i) to generate tree-c through
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hierarchical clustering with averaging and ii) to obtain the dissimilarity matrix (by the online tool:
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Show data) as the file online.dissimilarity. Further processes were all conducted in the R
the
R
package
(see
{rcdk::get.fingerprint}.
ChemMine
Tool
5
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environment. Details on the chemoinformatic tools are available in Guha (2007) and Guha and
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Cherlop–Powers (2016).
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Fuzzy set and fuzzy-weighting of color development patterns
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Based on {SYNCSA::belonging}, the multivariate matrix E = {eki}(e.g., functional matrix EC in
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this study or community matrix in general) can be weighted with the distance matrix D = {dij}.
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First, each element of D is normalized by the maximum element and the updated δij = dij/max(dij).
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Then, the distance matrix is converted to the similarity matrix S = {sij} where sij = 1 – δij. The
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“belonging” of item i to j fi (j) is calculated as the relative similarity of i to j: f i ( j )
si , j
s
; fi(j)
i, j
j
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is a fuzzy set. Finally, the multivariate matrix {eki}: the item (e.g., substrate) i of the sample k is
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weighted by the items j as ek ,i ' f i ( j )ek , j . j
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Permutation P value for detecting nonrandom results by chemical weighting
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Permutation P values were calculated as follows:
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B: Number of permutations to satisfy a certain condition
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b: Number of permutations such that R2perm ≥ R2obs
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b’: Number of permutations such that R2perm ≤ R2obs
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m: Number of permutations
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The permutation P value is the probability that B is the more extreme case than b or b’.
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PU = P(B ≤ b) = (b + 1)/(m + 1), and
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PL = P(B ≤ b’) = (b’+1)/(m + 1).
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When PU ≤ 0.025 or PL ≤ 0.025, we interpret the chemically weighted results as being
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statistically different from the randomly weighted results (according to the two-tailed test).
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References in Appendix D
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ftp://cran.r-project.org/pub/R/web/packages/SYNCSA/SYNCSA.pdf
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Electric Supplementary Material Statistical recipe for quantifying microbial functional diversity from EcoPlate metabolic profiling Takeshi Miki, Taichi Yokokawa, Po-Ju Ke, I Fang Hsieh, Chih-hao Hsieh, Tomonori Kume, Kinuyo Yoneya, Kazuaki Matsui
[email protected]
Additional Figures
Fig. S1 Miki et al.
(a)
(b) D−Galactonic−Acid−gamma−Lactone Tween−80 beta−Methyl−D−Glucoside alpha−Cyclodextrin alpha−D−Lactose D−Cellobiose Glycogen D−Xylose D−Galacturonic−Acid Glucose−1−Phosphate N−Acetyl−D−Glucosamine Tween−40 D−Malic−Acid alpha−Ketobutyric−Acid D−Mannitol i−Erythritol Itaconic−Acid L−Threonine L−Serine L−Asparagine D−Glucosaminic−Acid L−Arginine Glycyl−L−Glutamic−Acid alpha−Glycerol−Phosphate Putrescine gamma−Hydroxybutyric−Acid Pyruvic−Acid−Methyl−Ester Phenylethyl−amine L−Phenylalanine 4−Hydroxy−Benzoic−Acid 2−Hydroxy−Benzoic−Acid
(c) D−Mannitol
D−Malic−Acid
i−Erythritol
Itaconic−Acid
D−Malic−Acid
L−Asparagine
alpha−Ketobutyric−Acid
gamma−Hydroxybutyric−Acid
Itaconic−Acid
L−Threonine
L−Threonine
L−Serine
L−Serine
alpha−Ketobutyric−Acid
L−Asparagine
Pyruvic−Acid−Methyl−Ester
D−Glucosaminic−Acid
Glycyl−L−Glutamic−Acid
L−Arginine
L−Arginine
Glycyl−L−Glutamic−Acid
Phenylethyl−amine
alpha−D−Lactose
L−Phenylalanine
D−Cellobiose
4−Hydroxy−Benzoic−Acid
Glycogen
2−Hydroxy−Benzoic−Acid
alpha−Cyclodextrin
D−Galactonic−Acid−gamma−Lactone
beta−Methyl−D−Glucoside
D−Glucosaminic−Acid
D−Xylose
D−Galacturonic−Acid
D−Galacturonic−Acid
N−Acetyl−D−Glucosamine
Glucose−1−Phosphate
Glucose−1−Phosphate
D−Galactonic−Acid−gamma−Lactone
beta−Methyl−D−Glucoside
Tween−80
D−Mannitol
N−Acetyl−D−Glucosamine
D−Xylose
alpha−Glycerol−Phosphate
i−Erythritol
Putrescine
alpha−D−Lactose
Phenylethyl−amine
D−Cellobiose
L−Phenylalanine
alpha−Glycerol−Phosphate
4−Hydroxy−Benzoic−Acid
Glycogen
2−Hydroxy−Benzoic−Acid
alpha−Cyclodextrin
gamma−Hydroxybutyric−Acid
Tween−80
Tween−40
Tween−40
Pyruvic−Acid−Methyl−Ester
Putrescine
Fig. S1 Chemical similarity trees generated from different methods: (a) standard method in R function {rcdk:get.fingerprint}, (b) extended method in R function {rcdk:get.fingerprint, type="extended"}, and (c) online tool
Fig. S2 Miki et al.
(a)
(b)
(c)
0.18 0.16
non-weighted
0.14
R2
0.12
a b
0.1 0.08
a b c
tree-a tree-b tree-c
0.06 0.04
c
0.02 0 T=0.1 T=0.2 T=0.3 T=0.4 T=0.5 T=0.6 T=0.7 T=0.8 T=0.9
T=0.1 T=0.2 T=0.3 T=0.4 T=0.5 T=0.6 T=0.7 T=0.8 T=0.9
T=0.1 T=0.2 T=0.3 T=0.4 T=0.5 T=0.6 T=0.7 T=0.8 T=0.9
Avg
Max
Min
Final endpoint
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Fig. S2 Results of linear models linking multifunctionality with gene diversity in microcosms.
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R2 values from the linear model (MF ~ reduction of gene diversity) are compared for
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different metrics: (a) average, (b) maximum, and (c) minimum of triplicates. * denotes Pperm,U
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< 0.025 or Pperm, L < 0.025, respectively for tree x (x = a, b, c in Figure S1).
Fig. S3 Miki et al. Threshold T
0.9 0.7 0.5 0.9 0.7 0.5 0.9 0.7 0.5
0.9 0.7 0.5 0.9 0.7 0.5 0.9 0.7 0.5
0.9 0.7 0.5 0.9 0.7 0.5 0.9 0.7 0.5
0.5
UW CW-a CW-b CW-c
Constrained fractionof variance
0.55
*
0.45 0.4
*
*
0.35 0.3 0.25
Avg
Max
Min
Temporal integration
Avg
Max
Min
Temporal maximum
Avg
Max
Min
Final endpoint
Fig. S3 Results of db-RDA linking functional composition with month and treatment effects in forest soils, based on binarized data for different calculation methods. Statistical power (constrained fraction of variance) of distance-based RDA model (functional dissimilarity ~ treatment×month) varies depending on calculation methods. * Pperm,U < 0.025 or Pperm, L < 0.025 for tree x (x = a, b, c in Figure S1). Vertical and horizontal axes cross at a position corresponding to the average statistical power from default calculation methods (i.e., “Final endpoint and taking average of triplicates”). T values (= 0.9. 0.7, and 0.5) represent quantile-based threshold for the banalization. . The abbreviations are the same as in Fig. 5.
1.0
Fig. S4 Miki et al.
0.8
binary_uw
0.6
binary_cw
R2
cont_cwGuniFrac
0.4
cont_cwGuniFrac0.5 cont_cwF
0.0
0.2
cont_uw
0
5
10
15
20
25
30
Integration period (days)
Fig. S4 Sensitivity of db-RDA results to the integration period. Statistical power (R2) of distance-based RDA model is shown for different integration periods (0 –30 days). Maximum values among triplicates of forest soil data were used. For chemically weighted results, tree-c in Fig. S1 was used. Threshold T = 0.8 was used for binary results.
Fig. S5 Miki et al.
(a)
(b) D−Malic−Acid gamma−Hydroxybutyric−Acid Itaconic−Acid alpha−Glycerol−Phosphate D−Cellobiose D−Galacturonic−Acid alpha−Cyclodextrin Tween−40 L−Phenylalanine alpha−D−Lactose L−Threonine Glycogen alpha−Ketobutyric−Acid 2−Hydroxy−Benzoic−Acid D−Glucosaminic−Acid L−Serine Pyruvic−Acid−Methyl−Ester L−Asparagine Tween−80 D−Mannitol i−Erythritol Glucose−1−Phosphate Glycyl−L−Glutamic−Acid D−Galactonic−Acid−gamma−Lactone L−Arginine D−Xylose beta−Methyl−D−Glucoside Phenylethyl−amine 4−Hydroxy−Benzoic−Acid N−Acetyl−D−Glucosamine Putrescine
(c) Phenylethyl−amine L−Phenylalanine 2−Hydroxy−Benzoic−Acid D−Cellobiose alpha−Ketobutyric−Acid D−Galactonic−Acid−gamma−Lactone alpha−D−Lactose L−Threonine gamma−Hydroxybutyric−Acid D−Glucosaminic−Acid D−Xylose alpha−Cyclodextrin i−Erythritol L−Arginine D−Malic−Acid alpha−Glycerol−Phosphate Glucose−1−Phosphate N−Acetyl−D−Glucosamine beta−Methyl−D−Glucoside D−Mannitol Pyruvic−Acid−Methyl−Ester Glycogen Tween−80 Tween−40 Putrescine L−Asparagine 4−Hydroxy−Benzoic−Acid L−Serine D−Galacturonic−Acid Itaconic−Acid Glycyl−L−Glutamic−Acid
Glucose−1−Phosphate beta−Methyl−D−Glucoside Glycogen L−Threonine L−Phenylalanine D−Glucosaminic−Acid D−Cellobiose alpha−Cyclodextrin alpha−D−Lactose D−Malic−Acid L−Arginine D−Galactonic−Acid−gamma−Lactone alpha−Ketobutyric−Acid 4−Hydroxy−Benzoic−Acid N−Acetyl−D−Glucosamine Tween−80 Tween−40 L−Asparagine D−Mannitol L−Serine D−Galacturonic−Acid Pyruvic−Acid−Methyl−Ester Glycyl−L−Glutamic−Acid Phenylethyl−amine i−Erythritol D−Xylose Itaconic−Acid Putrescine 2−Hydroxy−Benzoic−Acid gamma−Hydroxybutyric−Acid alpha−Glycerol−Phosphate
Fig. S5 Chemical similarity trees based on pairwise correlations of color development patterns (color depths) between samples in EcoPlate incubations. Pairwise dissimilarity was calculated as 1 – max(r, 0) where r is the correlation coefficient. Similarity calculated in this way was interpreted to be based on the similarity of microbial responses to different substrates rather than those defined by chemical structure (see Fig. S1). Results were calculated using (a) temporally integrated data and maximum values of triplicates from forest soils, (b) final endpoint data and maximum values of triplicates from microcosm experiments, and (c) combined data from (a) and (b).
Fig. S6 Miki et al.
(a)
(b)
R2 of UW (0.0963)
(c)
R2 of UW 25
15
15
R2 of UW
5
0
R2 of CW-b (0.1629)
0
0
5
R2 of CW-a (0.1669)
0.00
0.05
0.10
0.15
R2 values
0.20
0.25
5
10
15
Density
10
10
20
R2 of CW-c (0.0731)
0.00
0.05
0.10
0.15
R2 values
0.20
0.25
0.00
0.05
0.10
R2 values
Fig. S6 Results of linear models linking multifunctionality with month and treatment effects in forest soils using permutated chemical dissimilarity matrices. Statistical power (R2) of linear model from chemically weighted multifunctionality (CW-a,b,c) is compared with power results from multifunctionality weighted by randomly permutated chemical-dissimilarity trees, to evaluate effectiveness of chemically weighted methods. We used data from Fig. 6a (temporal maximum with minimum of triplicates, and threshold T = 0.7; highlighted by a blue bar). Probability density distribution is from permuting tree-a (a) tree-b (b), and tree-c (c) as shown in Fig. S1. Pperm,U = 0.009, Pperm,U = 0.007, and Pperm,L = 0.107 for (a), (b), and (c), respectively. R2 of UW represents statistical power from chemically unweighted indices.
0.15
0.20
0.25
