Electronic supplementary material (ESM)

Biodiversity and multi-functionality in a microbial community: a novel

theoretical approach to quantify functional redundancy

Takeshi Miki

Institute of Oceanography, National Taiwan University

No.1 Sec. 4 Roosevelt Road, Taipei 10617, TAIWAN

Taichi Yokokawa

Center for Marine Environmental Studies (CMES), Ehime University

3 Bunkyo-cho, Matsuyama, Ehime 790-8577, JAPAN

Kazuaki Matsui*

Laboratory of Environmental Biological Science

Faculty of Science and Technology, Kinki University

3-4-1 Kowakae HigashiOsaka, Osaka 577-8502, JAPAN

*corresponding author: kmatsui@civileng.kindai.ac.jp

List of Tables and Figures

Appendix 1. Species sets assembled from MBGD

Appendix 2. Comparison of ortholog accumulation curves generated

by two methods

Appendix. 3 Supplementary methods

Tables S1-S6 Species sets used in community simulations

Figure S1 Comparison of ortholog accumulation curves of a bacterial

community generated by two methods

FigureS2 Multifunctionality predicted by ortholog reduction

Appendix 1. Species sets assembled from MBGD

The species sets for community simulations were based on MBGD default

species set (‘default as of 08/30/2011’) [46]. These species were classified into 16

habitat, and three oxygen requirement types (aerobic, anaerobic, or facultative). These

categories were primarily based on descriptions from HAMAP [47], and the UCSC

Archaeal genome browser [48]. Additional references for several species are also

cited (Table S1).

For the species set from tree-hole bacterial community [16](Table S2), each listed

bacterium is assigned to the closest MBGD strain as following procedure:

1) Same species is chosen from MBGD strain. A strain with some publication was

picked if multiple species were listed in MBGD.

2) When no species matched in listed MBGD strain, a strain with same genus is

chosen from MBGD strain. A closest MBGD strain was picked according to “The

All-species Living Tree” by Yarza et al. (2010).

3) When no genus matched in listed MBGD strain, a strain from same family is

chosen from MBGD strain. A closest MBGD strain was chosen according to “The

All-species Living Tree” by Yarza et al. (2010).

For the species sets from marine (Table S3 [50], Table S4 [49]) and freshwater

(Table S5 [2], Table S6; this study), the registered sequences in NCBI are assigned to

the most genetically related MBGD strains.

Reference

P. Yarza, W. Ludwig, J. Euzéby, R. Amann, K.-H. Schleifer, F.O. Glöckner & R.

Rosselló- Móra.2010 Update of the All-Species Living Tree Project based on 16S and

23S rRNA sequence analyses. Syst. Appl. Microbiol. 33 291-299.

Appendix 2. Comparison of ortholog accumulation curves generated by two

methods

We compared the method based on an analytical formula of the statistically

rigorous binomial mixture model (described in the main text) with another method,

which used a computationally intensive resampling algorithm (resampling method,

hereafter). This is equivalent to the older versions of the free software application

EstimateS (Colwell 1994-2004). In the resampling method, we randomly resampled

species to generate communities with any smaller species number (species subsets)

with 100 permutations for each species number. Subsequently, to test the power-law

relationship between SR and MF, we used a log-log linear regression of SR vs. MF

(i.e. ln[MF] = lnc + aln[SR]). Fitted data were all simulated results from permutations

in this resampling method. Then, we confirmed that the resampling and analytical

methods for generating ortholog accumulation curves generated nearly identical

results (Fig. S1). Variation among permutations in the resampling method for the

bacterial community was small, and approximated by the power-law relationship (Fig.

S1a). A closer examination of the ortholog accumulation curve also indicated the 95%

confidence interval (CI) from the analytical method, log-log linear regression on the

expected number from the analytical method, and log-log linear regression on

resampling method data exhibited almost identical trends (Fig. S1b). Consequently,

the results shown in the main text were derived from the following: a) the expected

value was calculated using the analytical method; b) the parameters (exponent and

intercept) were estimated using log-log linear regression on the expected number

generated from the analytical method; and 3) the results were based on these

estimated parameters.

Reference

Colwell, R.K. 1994-2004 EstimateS: statistical estimation of species richness and

shared species from samples. (http:purl.oclc.org/estimates)

Appendix 3. Supplementary Methods

Identification of bacterial isolates

Bacterial 16S rRNA genes were PCR amplified with primers 27F and 1492R

(Lane 1991). The PCR mixture (final volume, 50 L) contained, each

deoxynucleoside triphosphate at a concentration of 0.2 mM, each primer at a

concentration of 0.5 M, 1.25 U of TaKaRa Ex taq and 1 × Ex taq buffer (Takara

Biotechnology Co., Ltd.), and 1L of template, which was suspended pelleted cells

(the isolate cells was pelleted by centrifugation at 9000 rpm for 10 min, thereafter, the

cell pellet was re-suspend in 50 L TE buffer). Amplification of the genes started with

a denaturation step at 94°C (for 3 min), followed by 30 cycles of denaturation at 94°C

(45 sec), annealing at 55°C (45 sec), and an extension at 72°C (1.5 min). A final

extension at 72°C for 7 min completed the cycling. PCR products were purified with a

LaboPass PCR Purification Kit (COSMO GENETECH Co., Ltd.). Sequencing was

performed by Hokkaido System Science Co., Ltd. The primers used for sequencing

included the amplification primers mentioned above in addition to 515F (Turner et al.

1999) and 690R (Lane 1991). Sequence information obtained in this study has been

deposited in NCBI: accession numbers KF556684 to KF556703 for the bacterial

isolates. Each isolate was also assigned to the closest MBGD strain to construct the

species sets for community simulations (Table S6).

Evaluation of community-level multifunctionality by Biolog EcoPlateTM

An EcoPlate was composed with 31 response wells with different sole carbon

sources and a control well without carbon sources, in triplicate (Choi & Dobbs 1999).

A tetrazolium redox dye is included with the carbon source, which turns purple when

it is reduced by microbial respiration (Garland & Mills 1991). We withdrew ten-mL

aliquot from the each assembled bacterial community just after assembling the

bacterial communities. Hundred-L of an aliquot samples were applied to each wells

of a Biolog EcoPlate, and incubated for seven days at 25°C in the dark. At the end of

the incubation, absorbance of each wells were determined by microplate reader

(SH8100, CORONA Electric Co., Ltd. Japan). Relative absorbance of each carbon

source was calculated from average of absorbance in a carbon source (triplicate)

divided by average of absorbance in a control (triplicate), in a series of carbon sources.

The relative absorbance was used for further statistical analysis.

Reference

Choi, K.-H. & Dobbs, F. 1999 Comparison of two kinds of Biolog microplates (GN

and ECO) in their ability to distinguish among aquatic microbial communities. J.

Microbiol. Methods 36,203-213

Garland, J. L. & Mills, A. L. 1991 Classification and characterization of heterotrophic

microbial communities on the basis of patterns of community-level

sole-carbon-source utilization. Appl. Environ. Microbiol. 57, 2351-2359.

Lane, D.J. 1991 16S/23S rRNA sequencing. In E. Stackebrandt and M. Goodfellow

(eds.), Nucleic acid technique in bacterial systematic. John Wiley & Sons, Inc., New

York, NY. 115-175.

Turner, S., Pryer, K.M., Miao, V.P.W. & Palmer, J.D. 1999 Investigating deep

phylogenetic relationships among cyanobacteria and plastids by small subunit rRNA

sequence analysis. J. Eukaryot. Microbiol. 46 327-338.

Table S1. Default species set, comprised of 58 Achaea and 420 Bacteria species, from

the ortholog group assignment table (‘default’ as of 08/30/2011) from MBGD.

Table S2. Species set, comprised of 72 naturally occurring culturable bacteria from

tree-holes (Bell et al. 2005 [16])

Table S3. Species set, comprised of 16 bacteria (PCR-DGGE bands) from seawater

mesocosm (Riemann et al. 2000 [50]).

Table S4. Species set, comprised of 64 marine bacteria (16SrRNA & 16SrDNA

clones) from North Aegean Sea (Moeseneder et al. 2005[49]).

Table S5. Species set, comprised of 26 bacteria (PCR-DGGE bands) from freshwater

mesocosm (Riemann & Winding 2001 [2]).

Table S6. Species set, comprised of 20 naturally occurring culturable bacteria isolated

from freshwater pond in Matsuyama, Japan (this study).

Figure S1. Comparison of ortholog accumulation curves of a bacterial community

generated by two methods. (a) Species Richness (SR) vs. Ortholog Number (i.e.

multifunctionality MF) for the entire range of SR (1-420). Regression line on data

from the resampling method is logMF = 7.902 + 0.690 x logSR (r2 = 0.990, P <

0.001). (b) Magnified results from (a). The regression line on the estimated mean

value from the analytical method is logMF = 7.934 + 0.684 x logSR (r2 = 0.999, P <

0.001).

Figure S2. Multifunctionality predicted by ortholog reduction. The effect of ortholog

reduction on the average number of functions achieved above thresholds (T) where T

was based on the average functioning in each function across all treatments in each

experiment. P values from linear regression are < 0.05 (T = 0.5, 1.1), < 0.01 (T = 0.6),

< 0.001 (T = 1.0), < 10-4

(T = 0.7, 0.8, 0.9), 0.42 (T = 1.2), 0.44 (T = 1.5), 0.68 (T =

1.4), and 0.74 (T = 1.3), respectively. NS: non-significant (P > 0.05).

0 100 200 300 400

050

000

1000

0015

0000

Fig. S1 M.Y.M

Species richness

Num

ber o

f orth

olog

s

Species richness

Num

ber o

f orth

olog

s

(a) (b)

expected value from analytical methodlog-log regression on resampling methodmiddle 95% from 100 permutations

expected value from analytical method (mean)expected value from analytical method (95% CI)log-log regression on the analytical method log-log regression on the resampling method middle 95% from 100 permutations

200 210 220 230 240 25090

000

1000

0011

0000

1200

0013

0000

0 200 400 600 800 100012001400

05

1015

2025

30T = 0.5

Num

ber o

f fun

ctio

n ac

hiev

ed >

T

0 200 400 600 800 100012001400

05

1015

2025

30

T = 0.7

0 200 400 600 800 100012001400

05

1015

2025

30

T = 0.9

0 200 400 600 800 100012001400

05

1015

2025

30

T = 1.1

Ortholog richness reduction

Num

ber o

f fun

ctio

n ac

hiev

ed >

T

0 200 400 600 800 100012001400

05

1015

2025

30T = 1.3

Ortholog richness reduction

0 200 400 600 800 100012001400

05

1015

2025

30

T>1.5

Ortholog richness reduction

Fig. S2 M.Y.M

NS NS