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Supporting information - Materials and Methods
DNA extraction from isolated cultures
For bacteria, the enzyme-heat lysis method for preparation of cell-free DNA lysate
(Jeyaram et al. 2010) was used with additional treatment of 20 U of mutanolysin along with
lysozyme (Sigma-Aldrich). For yeasts, the above two enzymes were replaced by 50 U of
lyticase (Sigma-Aldrich). The DNA content was quantified spectrophotometrically using a
ND-1000 spectrophotometer (NanoDrop Technologies, Rockland, USA). The cell-free DNA
lysates with absorbance ratio (A260/280) of 1.8 to 2.2 were used as the template for PCR. For
LAB isolates which failed to give PCR amplification, the genomic DNA was extracted using
the method developed in this study for metagenomic DNA extraction, as described in the
subsequent sections, with the following modifications. Cells equivalent to 2 OD660 of 24  48
h old cultures were used for the extraction after washing with 0.1 M PBS buffer (pH 6.4).
Five KU of lysozyme (Sigma-Aldrich) and 20 U of mutanolysin (Sigma-Aldrich) were used
for bacteria, and 50 U of lyticase (Sigma-Aldrich) for yeasts. The initial cell lysis was done at
55 C for 2 h. DNA was stored at -20 C until further required.
Identification of culturable microorganisms
The culturable microorganisms were identified by amplified ribosomal DNA
restriction analysis (ARDRA) based grouping followed by rRNA gene sequencing. PCR
amplification was carried out in 25 µL final reaction volume containing 30  50 ng of the
genomic DNA as mentioned in the Supporting Information Table S1. Template free PCR
amplification was done for every set of PCR reaction. Four to five µL of the amplified
bacterial SSU rRNA gene and yeasts ITS1-5.8S-ITS2 amplicons were separately digested
with HaeIII, HinfI and CfoI (for bacteria), and with HaeIII, DdeI and TaqI (for yeasts)
(Promega, Madison, WI, USA) in 10 µL reaction volume as per manufacturer’s instructions.
The restriction patterns were analyzed by electrophoresis of the 10 µL reaction volume on 2.0
% (w/v) agarose gel in parallel with PCR 100 bp Low DNA ladder (Sigma-Aldrich) as
molecular size standard. Electrophoresis was run at 80 V for 2 h in 0.5  TBE [45 mM Trisborate, 1 mM EDTA (pH 8.0)] buffer. After staining in 0.5 µg mL-1 ethidium bromide
solution and destaining for 30 min each, the gel was documented using ChemiDoc MP (Bio
Rad, Hercules, USA). The sizes of the DNA fragments were measured using linear regression
method in Image Lab v4.0.1 software (Bio Rad). The restriction fingerprints were analyzed
using GelCompar II software, v6.5 (Applied Maths, Sint-Martens-Latem, Belgium). A
composite data set of the digestion profiles obtained from the three restriction enzymes was
generated with 2  3 % position tolerance. A dendrogram was created using UPGMA based
clustering of Jaccard similarity coefficient to cluster the isolates with similar ARDRA
profiles into phylotype groups. The cluster cut-off value was visually set at 50 % similarity
between the composite restriction patterns. Both qualitative (the different phylotype groups)
and quantitative (relative number of isolates per phylotype group) data were used for
determination of biodiversity estimates and statistical analyses.
The representative isolates from each phylotype group were randomly selected for
sequencing of bacterial SSU rRNA gene and yeasts LSU rRNA gene D1/D2 domain and
ITS1-5.8S-ITS2 regions using the conditions described in Supporting Information Table S1.
The amplified products were purified using NucleoSpin Extract II gel extraction kit
(Macherey-Nagel, Düren, Germany) following manufacturer’s instructions. The sequencing
reactions were performed (Merck, Bangalore, India) to cover the full length of the target
regions using multiple primers. The full length sequences were generated by assembling the
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partial sequences into contigs using DNA Baser v3 software (Heracle BioSoft SRL, Arges,
Banat). The base calls of the sequences were validated using Chromas LITE v2.01 software
(www.technelysium.com.au). The assembled bacterial sequences were quality-checked for
the presence of chimera using Pintail v1.0 and DECIPHER softwares
(http://decipher.cee.wisc.edu/FindChimeras.html) (Wright et al. 2012). To designate the
taxonomic status of the isolates the sequences were queried against NCBI's non-redundant,
reference RNA sequence database (refseq_rna) using BLASTN algorithm
(http://blast.ncbi.nlm.nih.gov/Blast.cgi) with 98 % similarity as identification threshold. Any
ambiguous identification arises was confirmed using Ribosomal RNA Database Project
release 10 (http://rdp.cme.msu.edu/seqmatch/seqmatch_intro.jsp) and CBS yeast nucleotide
database (http://www.cbs.knaw.nl/Collections/). The identified sequences were aligned using
Clustal X algorithm implemented in MEGA5 along with the sequences of the nearest known
taxa, and a neighbour joining tree was constructed based on the evolutionary distance
calculated using Kimura-2-parameter substitution model (Tamura et al. 2011).
Determination of DGGE band identity
The DGGE bands of interest were excised from the polyacrylamide gel using sterile
scalpel and the DNA was eluted in 50 µL sterile deionized water by overnight incubation at 4
C. Two µL of the eluted DNA was re-amplified using the conditions described previously.
The re-amplified products were checked for the quality in agarose gel, presence and position
of the selected band by running in DGGE and comparing with parent DGGE profile. The
above elution and re-amplification steps were repeated twice or until getting a pure single
band that co-migrated with parent DGGE band, after which the PCR products were purified
and sequenced using M13 primer as described in the Supporting Information Table S1. The
closest known taxonomic identities of the DGGE bands were determined by sequence
similarity search as previously described. The DGGE bands that produced reproducible
multiple-band profile on subsequent elution steps were declared as heteroduplexes.
Determination of microbial diversity estimates
To check the changes in the bacterial diversity during the fermentation, the
cultivation-dependent and the cultivation-independent PCR-DGGE data were used for the
calculation of richness estimates and diversity indices using EstimateS v9.1.0 software
(Colwell 2013). For the culturable-based data, the number of different phylotype groups and
relative number of isolates per phylotype group were used for the calculations. For the
culture-independent data, the absence/presence and quantitative densitometric values of
intensities of different DGGE bands were used for the calculations. Evenness (Shannon index
of evenness) was calculated using the formula eH´/N, where H´ is the Shannon diversity index
and N is the observed number of phylotypes or DGGE bands. The percentage of coverage
was calculated by Good’s method using the formula [1(n/N)]100, where n is the number of
phylotypes represented by one isolate (singletons) present in each fermentation stage and N is
the total number of isolates in that fermentation stage.
Illumina data analysis
The raw paired-end reads obtained as fastq files from the Illumina MiSeq platform
were joined to generate the V4-V5 targeted amplicons and demultiplexed to assign the reads
according to the sample by using the web-based file processing tools of the MG-RAST
metagenomic analysis server (Meyer et al. 2008). The sequences were quality-filtered based
on length, number of ambiguous bases and phred quality scores using the default pipeline
options. Sequences having base calls with phred quality score, Q < 15 were filtered out.
Quality-filtered sequences were then passed through MG-RAST’s rRNA pipeline under
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default parameters for secondary filtering to remove non-rRNA sequences. Sequences with
less than 70 % identity to rRNA sequences from the databases of Greengenes, SILVA (SSU
and LSU) and RDP were pre-screened as non-rRNA sequences. rRNA sequences were
clustered into operational taxonomic units (OTUs) at 97 % identity threshold using the uclust
algorithm (Edgar 2010) and the longest sequence in each OTU was chosen as the OTU
representative sequence. Taxonomic annotations of the OTUs were carried out using the
“best hit classification” method. BLAT similarity search algorithm (Kent 2002) with
minimum alignment length cut-off of 100 bp and maximum e-value cut-off of 1e-5 was used
to assign species-level taxonomic annotation at 97 % similarity threshold against the nonredundant multi-source M5RNA rRNA database implemented in MG-RAST. The MG-RAST
generated OTU table was used for various downstream analyses. To assure a higher level of
accuracy during subsequent analyses, the OTU table was quality filtered to remove
eukaryota-specific (chloroplast and mitochondria origin) OTUs and taxonomically
unassigned OTUs that did not match any reference sequences in the databases. Only those
taxa that had an average relative abundance of 1 % or greater across the samples studied are
indicated; taxa with less than 1 % relative abundance are combined together. The relative
abundance of OTUs were analyzed at various taxonomic levels (family, genus and species)
and studied by PCA using PAST v3.02 software. Hierarchical clustering of the OTUs and the
stage-wise samples were performed using the complete linkage algorithm with the euclidean
distance matrix calculated from the normalized relative abundance and a heat map was
generated to depict the change in microbial community structure during the fermentation in R
environment (http://www.r-project.org/) using the "gplots" package. Data were normalized by
log10 (xi+1) transformation.
For alpha diversity analysis and generation of alpha rarefaction curves, the qualityfiltered OTU table at species-level was rarefied at a range of depth of 100  8670 reads per
sample using the multiple_rarefactions.py script in QIIME v1.8.0 bioinformatics pipeline
(Caporaso et al. 2010). The depth is selected based on the lowest number of quality-filtered
reads assigned amongst the samples analyzed. In alpha diversity analysis, two richness
estimates (observed species and Chao1), three diversity indices (Fisher alpha, Simpson's
reciprocal and Shannon), evenness (Shannon's equitability) and estimated sample coverage
(Good's coverage) were calculated.
References:
Jeyaram K, Romi W, Singh TA, Devi AR, Devi SS (2010) Bacterial species associated with
traditional starter cultures used for fermented bamboo shoot production in Manipur state
of India. International Journal of Food Microbiology, 143, 18.
Caporaso JG, Kuczynski J, Stombaugh J et al. (2010) QIIME allows analysis of highthroughput community sequencing data. Nature Methods, 7, 335336.
Kent WJ (2002) BLAT the BLAST-like alignment tool. Genome Research, 12, 656664.
Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST.
Bioinformatics, 26, 24602461.
Meyer F, Paarmann D, D'Souza M et al. (2008) The metagenomics RAST server - a public
resource for the automatic phylogenetic and functional analysis of metagenomes. BMC
Bioinformatics, 9, 386.
Colwell RK (2013) EstimateS: Statistical estimation of species richness and shared species
from samples. Version 9 and earlier. User’s guide and application. Available from:
http://viceroy.eeb.uconn.edu/estimates/.
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Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular
evolutionary genetics analysis using maximum likelihood, evolutionary distance, and
maximum parsimony methods. Molecular Biology and Evolution, 28, 27312739.
Wright ES, Yilmaz LS, Noguera DR (2012) DECIPHER, a search-based approach to chimera
identification for 16S rRNA sequences. Applied and Environmental Microbiology, 78,
717725.
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