Bacterial community dynamics during industrial ...

Food Microbiology 39 (2014) 39e46

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Bacterial community dynamics during industrial malting, with an emphasis on lactic acid bacteria A. Justé a, b, c, d, S. Malfliet c, d, e, M. Waud a, b, c, d, S. Crauwels a, b, c, d, L. De Cooman c, d, e, G. Aerts c, d, e, T.L. Marsh f, S. Ruyters a, b, c, d, K. Willems a, b, c, d, P. Busschaert a, b, c, d, B. Lievens a, b, c, d, * a

Laboratory for Process Microbial Ecology and Bioinspirational Management (PME&BIM), Department of Microbial and Molecular Systems (M2S), KU Leuven, Campus De Nayer, B-2860 Sint-Katelijne-Waver, Belgium b Scientia Terrae Research Institute, B-2860 Sint-Katelijne-Waver, Belgium c Leuven Food Science and Nutrition Research Centre (LFoRCe), KU Leuven, Belgium d Leuven Institute for Beer Research (LIBR), KU Leuven, Belgium e Laboratory of Enzyme, Fermentation, and Brewing Technology (EFBT), M2S, KU Leuven, KAHO Sint-Lieven Technology Campus, B-9000 Gent, Belgium f Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA

a r t i c l e i n f o

a b s t r a c t

Article history: Received 8 February 2013 Received in revised form 9 October 2013 Accepted 30 October 2013 Available online 7 November 2013

Characterization of the microflora during malting is an essential step towards process management and optimization. Up till now, however, microbial characterization in the malting process has mostly been done using culture-dependent methods, probably leading to biased estimates of microbial diversity. The aim of this study was to characterize the bacterial communities using two culture-independent methods, including Terminal Restriction Fragment Length Polymorphism (T-RFLP) and 454 pyrosequencing, targeting the 16S rRNA gene. Studied samples originated from two harvest years and two malting houses malting the same batch of barley. Besides targeting the entire bacterial community (T-RFLP), emphasis was put on lactic acid bacteria (LAB) (T-RFLP and 454 pyrosequencing). The overall bacterial community richness was limited, but the community structure changed during the process. Zooming in on the LAB community using 454 pyrosequencing revealed a total of 47 species-level operational taxonomic units (OTUs). LAB diversity appeared relatively limited since 88% of the sequences were covered by the same five OTUs (representing members of Weissella, Lactobacillus and Leuconostoc) present in all samples investigated. Fluctuations in the relative abundances of the dominant LAB were observed with the process conditions. In addition, both the year of harvest and malting house influenced the LAB community structure. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: 454 pyrosequencing Lactic acid bacteria (LAB) Malt microflora Microbial community analysis Terminal restriction fragment length polymorphism (T-RFLP)

1. Introduction Malting is a complex biological process involving many biochemical and physiological reactions, leading to the synthesis of hydrolytic enzymes and degradation of the grain structure. Technically, three steps are involved in malting: steeping, germination and kilning. After cleaning and calibration of the grain kernels (mainly barley), grains are submerged and aerated until a water content of 42e46% is reached (steeping). In general, water temperatures of 10e15 C and steeping times of 24e48 h are used. The

* Corresponding author. Laboratory for Process Microbial Ecology and Bioinspirational Management, Fortsesteenweg 30A, B-2860 Sint-Katelijne-Waver, Belgium. Tel.: þ32 15 305590; fax: þ32 15 305099. E-mail address: [email protected] (B. Lievens). 0740-0020/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.fm.2013.10.010

grains are then allowed to germinate under humid and aerobic conditions at 16e20 C for 3e6 days, resulting in enzymatic breakdown of endosperm cell walls and proteins. Germination is ended by drying the grains (kilning) for approximately 21 h at temperatures increasing gradually from about 50 to 85 C or more. Kilning stops the biochemical reactions and ensures stability and storability of the dried product. During this step, several colour and flavour compounds are produced, thereby influencing the characteristics of the final beer (Laitila et al., 2011). In addition to the germinating grains, a diverse microbial community (Flannigan, 2003; Laitila et al., 2006b; Laitila, 2007; Noots et al., 1998) represents a second metabolically active compound in the malting ecosystem. Microorganisms greatly affect malting performance and malt quality, and thus also beer quality. Depending on the nature and extent of the microorganisms present, their effects may be either beneficial or disadvantageous to the process and/or the final

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A. Justé et al. / Food Microbiology 39 (2014) 39e46

product (Boivin and Malanda, 1997; Flannigan, 2003; Laitila et al., 1997, 2007; Lowe and Arendt, 2004). Consequently, more insight into the microbial communities that are involved in malting may contribute to the improvement of malt characteristics and a safe malting process and beer (Laitila et al., 2011). In general, the microbial load and composition of barley and malt have been determined using traditional microbiological methods based on plating, counting and identifying colonies (Justé et al., 2011). As these techniques rely on the culturability of the organisms, our view on the total microflora in the malting ecosystem is probably heavily biased and might be different from reality (Rappé and Giovannoni, 2003). Therefore, these classical approaches are increasingly being complemented or replaced by culture-independent, molecular methods (Justé et al., 2008). Fingerprinting techniques like Terminal Restriction Fragment Length Polymorphism (T-RFLP) and Denaturing Gradient Gel Electrophoresis (DGGE), often complemented with sequencing, have been widely used to describe the diversity and dynamics of microbial communities in all kind of ecosystems and habitats (Jany and Barbier, 2008; Justé et al., 2008; Ranjard et al., 2000; Tolvanen and Karp, 2011). More recently, technological advances such as 454 amplicon pyrosequencing have enabled rapid characterization of microbial communities at a greater sequence depth than was deemed possible via cloning and Sanger sequencing, enabling highly efficient in-depth microbial community analysis (Sogin et al., 2006). Surprisingly, with the exception of only a few studies using fingerprinting techniques (Laitila et al., 2007; Kaur, 2009), these modern techniques have not yet been used to investigate microbial communities during malting. One important group in many food applications is the lactic acid bacteria (LAB), which have as a common metabolic property the production of lactic acid from the fermentation of carbohydrates (Carr et al., 2002). LAB are Gram positive, catalase-negative, nonsporulating, and acid tolerant bacteria that belong to the Firmicutes, including members of for example Lactobacillus, Lactococcus, Leuconostoc, Pediococcus, Streptococcus and Weissella (Stiles and Holzapfel, 1997; Axelsson, 1998; Rouse et al., 2007). LAB are commonly exploited for the bio-preservation of various foods, feed and beverages (Rouse et al., 2007). In addition, LAB are used in the production of probiotic foods (Rathore et al., 2012). In the malting and brewing industry, LAB strains have been extensively used for several reasons (Lowe and Arendt, 2004). One example is the development and use of LAB starter cultures as inoculants during the malting process in order to improve the malt quality and safety (Boivin and Malanda, 1997; Haikara and Laitila, 1995). Biological control methods using LAB have shown high promise for the control of spoilage organisms or toxigenic fungi like fusaria, both in malting and in brewing (Dixon, 1959; Haikara et al., 1993; Haikara and Laitila, 1995; Laitila et al., 1997; Lowe and Arendt, 2004). Furthermore, certain LAB produce antimicrobial substances which restrict the growth of harmful bacteria that compete with grain tissue for dissolved oxygen and may also retard mash filtration (Lowe and Arendt, 2004; Van Campenhout, 2000). Also Laitila and co-workers demonstrated an enhanced malt processing potential after LAB addition to the steeping water (Laitila et al., 2006a; Raulio et al., 2009). Although the importance of LAB is highly recognized in the malting and brewing industry, so far most studies have focused on individual isolates (Booysen et al., 2002; Rouse et al., 2007), while complete LAB communities that are associated with barley and the malting process have not yet been studied in detail. In this study, we investigated the structure and dynamics of the bacterial communities, and also more specifically the LAB communities, associated with industrial malting, i.e. from barley up till the final malt using T-RFLP. In addition, the endogenous LAB community was deeply characterized using 454 pyrosequencing of

16S ribosomal RNA genes. Study samples were obtained from two harvest years and two different malting houses exhibiting a different germination regime. 2. Material and methods 2.1. Study samples Both in 2010 and 2011, barley and malt samples were obtained from an industrial malting of the barley variety Sebastian (French harvest). Samples were obtained from two different malting houses exploited by the same malting company, in which grains from the same barley batch were malted. These malting houses represented a system with isolated, closed germination rooms (further referred to as malting house “M1”) and a system with open germination rooms in which simultaneously barley from other batches and/or varieties was germinated (further referred to as malting house “M2”). Samples were taken at different steps of the malting process, i.e. from barley, 1 day germinated barley, 5 days germinated barley (also called green malt), and the final kilned malt. For each step, multiple samples were randomly taken, pooled (resulting in a total of about 300 g) and transported to the laboratory for further processing. 2.2. DNA extraction Ten randomly taken kernels of each pooled sample were soaked in TriseHCl buffer (pH 8; 10 mM) in a 2 ml screw cap tube for 2 h at 4 C to improve sample pulverization. Rootlets of germinating kernels were removed. Next, samples were mechanically disrupted by reciprocal shaking for 30 s after addition of 75 ml glass beads (212e300 mm) using a Fast Prep instrument (Thermo Savant, Holbrook, NY, USA). Subsequently, a subsample of 0.1 g from each pulverized sample was subjected to DNA extraction using the MoBio PowerSoilÒ DNA isolation kits (MoBio Laboratories, Inc., Solana Beach, CA, USA) according to the manufacturer’s instructions with slight modifications as homogenization was performed with a Fast Prep instrument at maximum speed for four times 30 s. DNA extracts were stored at 20 C until further use. 2.3. Terminal restriction fragment length polymorphism (T-RFLP) Two primer sets targeting the 16S ribosomal RNA (rRNA) gene were used for T-RFLP analysis, including the universal bacterial primer set 516F (50 -TGCCAGCAGCCGCGGTA-30 ; 50 FAM-labelled) (Nagashima et al., 2003) and 1541R (50 -AAGGAGGTGATCCAGCC30 ) (McCaig et al., 2001), and the LAB specific primer set 7F (50 AGAGTTTGATYMTGGCTCAG-30 ; 50 HEX-labelled) and 677R (50 CACCGCTACACATGGAG-30 ) (Heilig et al., 2002). Whereas 7F is nonspecific, 677R has been specifically developed to target four important LAB genera, including Lactobacillus, Leuconostoc, Pediococcus, and Weissella (Heilig et al., 2002). PCRs were performed on a Biorad T100 thermal cycler in a 20 mL reaction volume, containing 0.15 mM of each dNTP (Invitrogen, Merelbeke, Belgium), 0.5 mM of each primer, 1 unit Titanium Taq DNA polymerase, 1 Titanium Taq PCR buffer (Clontech Laboratories, Inc., Palo Alto, CA, USA) and 1 mL genomic DNA. Samples were denatured at 94 C for 2 min and then subjected to 30 cycles of 45 s at 94 C, 45 s at 64 C (universal primers) or 66 C (LAB primers), and 45 s at 72 C, with a final extension at 72 C for 10 min. Subsequently, labelled PCR products (approximately 200 ng) were digested for 4 h at 37 C with either MspI or HinfI (New England Biolabs, Frankfurt am Main, Germany). Restriction fragment analyses were conducted on an Applied Biosystems 373A Automated Sequencer (two technical replicates).


Data matrices were visualized and peak areas were expressed as relative values within each sample using the T-REX software program (Culman et al., 2009; http://trex.biohpc.org/). T-RFLP profiles were aligned by inspection of the electropherograms and by manual grouping of the peaks into categories. Webcutter (http:// rna.lundberg.gu.se/cutter2/) was used to predict restriction enzyme sites within chloroplast DNA amplified by the primer pairs used. Following removal of the terminal restriction fragments (TRFs) corresponding to this non-target DNA, profiles were compared using the Sørensen similarity index (Sørensen, 1948) and clustering was performed using the unweighted pair group method with arithmetic mean (UPGMA). In order to distinguish true peaks from noise, data analysis was restricted to the TRFs representing more than 1% of the total peak area obtained per sample. Identification of the microbial taxa representing dominant TRFs was based on an in silico digest database generated by the virtual digest tool from MiCA (Shyu et al., 2007; available at http://mica.ibest.uidaho. edu/digest.php) of good-quality 16S rRNA gene sequences compiled by the RDP Release 10 (Cole et al., 2009) in combination with the 454 sequencing results obtained in this study. 2.4. 16S rRNA gene amplicon pyrosequencing In addition to T-RFLP analysis, samples were subjected to 454 pyrosequencing in order to assess the LAB community associated with malting in detail. Samples were amplified using barcodetagged fusion primers that represented the universal bacterial forward primer 341F (50 -CCTACGGGAGGCAGCAG-30 ) (Muyzer et al., 1996) and the LAB specific reverse primer 677R. In this way amplification resulted in a product of about 423 bp, covering the V3eV4 region of the 16S rRNA gene, which is suited for 454 pyrosequencing (read length limited to about 450 bp due to technical limitations (Youssef et al., 2012)). More specifically, the forward fusion primer (52 bp) consisted of the “A” adaptor, amended with a unique barcode and the original primer sequence (341F). The reverse fusion primer (52 bp) consisted of the “B” adaptor, amended with a barcode and the original primer sequence (677R). PCR amplification was performed in duplicate for each DNA extract (repeats having the same forward primer sequence and a different barcode in the reverse fusion primer) as described above (annealing temperature of 66 C). Following verification of the PCR product by agarose gel electrophoresis, amplicons were purified using the Qiagen PCR purification kit. Purified amplicons were quantified using the Qubit fluorometer (Invitrogen) and pooled for pyrosequencing in equimolar quantities. Pyrosequencing using the 454/ Roche GS FLX Titanium chemistry was carried out according to the manufacturer’s instructions. Obtained sequences were assigned to the corresponding sample based on the 50 and 30 barcodes using Biopython, not allowing any error in the sequence corresponding to the barcode. Further sequence processing and clustering was performed in Mothur version 1.27 (Schloss et al., 2009). Sequences were trimmed based on a minimum Phred score of 25 (i.e. 0.3% error rate) over a 50 bp moving window. Minimum and maximum length was set to 315 and 330 nucleotides. Sequences with ambiguous base calls and homopolymers longer than 8 nucleotides were rejected. After removal of chimeras detected by the Uchime chimera detection program (Edgar et al., 2011) sequences were aligned to the SILVA reference alignment and grouped into operational taxonomic units (OTUs) based on a 3% sequence dissimilarity cut-off using the furthest neighbour algorithm. Obtained OTUs were identified by querying representative sequences for each OTU against GenBank using BLAST (excluding uncultured/environmental database entries) (Altschul et al., 1990) and by using the Ribosomal Database Project (RDP) website (http://rdp.cme.msu. edu/; Cole et al., 2009). Only sequences corresponding to LAB

41

sequences were retained in the dataset (representing c. 97.5% of all sequences obtained), and data from the two replicates were pooled, whereas singletons (i.e., OTUs represented by only 1 sequence across all samples) were removed. Rarefaction curves (Colwell and Coddington, 1994) were generated using Mothur and R (R Development Core Team, 2010) assessing the adequacy of sampling as well as the OTU richness. With the exception of a few samples (e.g., G1d_0_M2, G1d_0_M1 and M_0_M1), rarefaction curves were generally tending to saturation (Fig. S1; singletons included), suggesting that the sequencing depth was sufficient enough to accurately detect the dominant LAB OTUs. As for the TRFLP analysis, UPGMA clustering of the relative abundances was performed using Sørensen’s distance. 3. Results and discussion 3.1. T-RFLP analysis In order to get a first view on the structure of the microbial communities occurring during industrial malting as well as to investigate their dynamics, a T-RFLP analysis was conducted, targeting either the entire bacterial community or the LAB community. 3.1.1. Entire bacterial community Across all 14 samples investigated, 38 different bacterial TRFs were obtained for MspI, while 53 TRFs were recorded for HinfI, with similar results obtained for both technical repeats. The number of TRFs per sample varied between 4 and 16 for MspI and from 6 to 26 for HinfI showing a relatively limited bacterial diversity. Almost all samples were characterized by the same few dominant TRFs (Fig. 1; exemplified by HinfI digestion; similar results were obtained for the MspI digestion (data not shown)). Six TRFs were commonly detected in the different samples: TRFs of 136, 332, 630, 738, 796 and 824 bp, corresponding to an average (across all samples) relative peak area of 4, 3, 7, 16, 18 and 20%, respectively. Whereas the TRFs of 136, 738 and 824 bp were found in all samples, the 796 bp TRF was found in all samples except for the barley samples (Fig. 1). These commonly found TRFs were found to correspond with members of the genera Enterobacter and Sphingobacterium (136 bp TRF), Weissella, Lactobacillus, Lactococcus and Streptococcus (738 bp TRF), Acinetobacter and Stenotrophomonas (796 bp TRF), and Leuconostoc and Pseudomonas (824 bp TRF). TRFs of 332 and 630 bp corresponded to members of Wautersiella and Cryseobacterium, and Curtobacterium and Propionobacterium, respectively. These results are in line with the study performed by Kaur (2009) in which 32 cloned bacterial 16S rRNA genes from three Australian malt samples revealed members of the same bacterial groups as found in this study. In addition, bacteria like Lactobacillus, Lactococcus, Streptococcus and Weissella have been frequently found in the malting process (Angelino and Bol, 1990; Bokulich and Mills, 2012; Booysen et al., 2002; O’Sullivan et al., 1999; Petters et al., 1988). In all samples, at least 75% of the peak area was covered by TRFs that were also found in the barley, suggesting an important role for field and storage microflora during malting. Strikingly, the TRF of 115 bp (which could not be identified by the virtual digest tool at the MiCA website), representing one of the minor TRFs found in the barley, was only found in one additional sample (1 day germinated barley, harvest year 2011, M2), where it represented about 34.3% of the peak area. So far, we have no explanation why this TRF was specifically more abundant in this sample compared to the other samples. Examination of more samples could help to explain this observation. In order to assess potential differences between the bacterial community structure of the different samples, T-RFLP profiles


Fig. 1. Relative abundance and distribution of the most dominant bacterial terminal restriction fragments (TRFs) obtained by T-RFLP analysis (HinfI digestion; mean values of two technical replicates) of different samples taken during industrial malting of the French Sebastian barley variety harvested in (A) 2010 and (B) 2011. Only TRFs with a cumulative peak surface area exceeding 10% of the total cumulative surface area (over all samples) are specifically indicated to keep the figure displayable; other TRFs are grouped together in “other TRFs”. Samples are annotated by the nature of the sample (B ¼ barley; G1d ¼ 1 day germinated barley; G5d ¼ 5 days germinated barley; M ¼ malt), harvest and malting year (0 ¼ 2010; 1 ¼ 2011) and malting house (M1 and M2).

(combined datasets obtained with both restriction enzymes) were clustered based on Sørensen similarity indices (Sørensen, 1948). Clustering revealed that samples grouped together by process step (Fig. 2A), indicating shifts in the bacterial community structure during the different malting steps. The bacterial communities of barley samples from both harvest years were highly similar and clustered separately from the other samples, suggesting a particular community associated with barley of the Sebastian variety grown in France. As for the barley samples, all malt samples from both harvest years and both malting houses clustered separately in one cluster and also the germinating barley samples formed a separate cluster, with the exception of the two 5 days germinated barley samples from 2010 (G5d_0_M1 and G5d_0_M2, having 3 and 20% similarity with the other germinated barley samples, respectively) (Fig. 2A). More specifically, G5d_0_M2 showed more similarity with the malt samples while G5d_0_M1 clustered separately. Remarkably, the sample of the final malt obtained from the latter (M_0_M1) clustered together with the other malt samples, illustrating the specific community that was obtained for the malt samples. Overall, the malt samples were more similar to the barley samples, rather than to the germinated barley samples, having community similarities of 34 and 18%, respectively (Fig. 2A). Bacterial communities in barley and germinated barley exhibited 18% similarity. These shifts in microbial community structure may be

Fig. 2. Unweighted-pair group method with arithmetic mean (UPGMA) dendrogram derived from Sørensen similarity coefficients of (A) combined MspI and HinfI T-RFLP profiles from the bacterial communities, (B) MspI T-RFLP profiles from the lactic acid bacteria and (C) 454 pyrosequencing output for the lactic acid bacteria occurring in industrial malting of the French Sebastian barley variety. Samples are annotated by the nature of the sample (B ¼ barley; G1d ¼ 1 day germinated barley; G5d ¼ 5 days germinated barley; M ¼ malt), harvest and malting year (0 ¼ 2010; 1 ¼ 2011) and malting house (M1 and M2).

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explained by the different conditions to which the kernels are exposed during industrial malting. For example, the conditions during germination with temperatures of 15e20 C and a high humidity are very different from the conditions during barley storage and kilning, which may give rise to a changing microbial community throughout the malting process. Likewise, it has also been observed that the microbial load during germination is much higher than in malt and in barley which has the lowest microbial counts (Wilhelmson et al., 2003). 3.1.2. LAB community In total LAB targeted T-RFLP revealed 30 different TRFs, with 4e 15 TRFs per sample when MspI was used. When HinfI was used, not all samples resulted in reliable electropherograms and showed low total peak areas. Therefore, further analyses were restricted to MspI digestion. In comparison with the analysis of the entire bacterial community, amplicons were generated here using a LAB-specific primer pair (7F-677R), selectively targeting Lactobacillus, Leuconostoc, Pediococcus, and Weissella, while excluding other taxa (Heilig et al., 2002). Similar to the entire bacterial community, the majority of the most dominant TRFs was shared by all samples investigated (Fig. 3). Nevertheless, in contrast to the entire bacterial community, samples appeared to be more scattered over the dendrogram, with no pronounced clusters for the different process

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steps (Fig. 2B). As T-RFLP analysis is based on amplicon length variations that are produced after restriction digestion and different primer pairs were used for analyzing the entire bacterial community and the LAB community, further in-depth comparison of both datasets was not performed. The three most dominant TRFs obtained corresponded to fragment lengths of 553, 578 and 583 bp and represented an average (across all samples) relative peak area of 39, 15 and 23%, respectively (Fig. 3). Based on in silico digestion these TRFs corresponded to the putative presence of Leuconostoc (pseudo)mesenteroides, several Lactobacillus species including Lactobacillus plantarum, and Weissella species, respectively. The 553 bp TRF corresponding to Leuconostoc was found as the most abundant TRF across all samples investigated (Fig. 3), which is in line with Kaur (2009) who reported Leuconostoc as the most dominant LAB genus in malt, with Booysen et al. (2002) based on identification of isolates recovered on De Man Rogosa Sharpe (MRS) agar and with Bokulich and Mills (2012) based on T-RFLP of two malt extracts, representing 15 and 30% of the relative peak areas. Leuconostoc species are known as ropy slime producers, but might also have a favourable effect on the aroma of distilled drinks such as Scotch Whisky (Priest and Pleasants, 1988). In addition, some Le. pseudomesenteroides strains have been isolated from malt showing bacteriocin production, having the potential to be used in the brewing industry to enhance the microbiological stability of beer (Rouse et al., 2007). 3.2. 16S rRNA gene amplicon pyrosequencing for the LAB community

Fig. 3. Relative abundance and distribution of the most dominant terminal restriction fragments (TRFs) representing lactic acid bacteria obtained by T-RFLP analysis (MspI digestion; mean values of two technical replicates) of different samples taken during industrial malting of the French Sebastian barley variety harvested in (A) 2010 and (B) 2011. Only TRFs with a cumulative peak surface area exceeding 10% of the total cumulative surface area (over all samples) are specifically indicated; other TRFs are grouped together in “other TRFs”. Samples are annotated by the nature of the sample (B ¼ barley; G1d ¼ 1 day germinated barley; G5d ¼ 5 days germinated barley; M ¼ malt), harvest and malting year (0 ¼ 2010; 1 ¼ 2011) and malting house (M1 and M2).

Because of their importance and high relative abundance observed for our samples, a detailed characterization of the LAB community was pursued by 454 pyrosequencing. Using a 3% sequence dissimilarity cut-off value, obtained sequences were grouped in 150 different OTUs of which 113 were LAB-associated. In general, non-LAB sequences represented only a small fraction of the sequences obtained for each sample (about 1.5%), except for the barley samples (up to 13%). This might be due to relatively low LAB loads in the barley samples. Indeed, classical plate counts on MRS agar, a medium designed for detection of LAB (De Man et al., 1960), revealed microbial counts of about 103 cfu/g in barley, while counts of 106e8 cfu/g were recorded during germination or in the malt (data not shown). Plating on a general medium, on the other hand, resulted in 107 cfu/g in barley. Consequently, it may be assumed that DNA of non-target populations could be amplified, due to primer mismatching. In addition, amplicon yields for the barley samples were very low, indicating low template amounts in the samples. After removing singletons, a set of 47 LAB-associated OTUs, representing a total of 33,624 sequences (average of 317 bp), was retained for further analysis, with sequence numbers varying from 517 to 4874 (average 2402) and from 2 up to 9548 per sample and OTU (across all samples), respectively (Table 1). Out of the 47 OTUs identified, 14 OTUs represented >0.1% of the sequences obtained in this study (further referred to as “dominant OTUs”). These dominant OTUs covered 98.3% or more of the LAB sequences obtained per sample (Fig. 4; Table 1). In all samples investigated, at least 97% of the sequences were also associated with the barley samples, suggesting an important contribution of the barley in the microbial community composition during the malting process as was also suggested by T-RFLP analysis. Moreover, the five most dominant OTUs (representing members of Weissella (OTU 111e113), Lactobacillus (OTU 104) and Leuconostoc (OTU 109)) were present in all samples investigated and covered 88% of all sequences obtained (Fig. 4). Weissella was found as the most dominant LAB genus during malting, represented by three dominant OTUs (OTU 111e113) (in

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Table 1 Lactic acid bacteria operational taxonomic units (OTUs)a identified in this study. Phylogenetic affiliationb

OTU

OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU OTU a b c d

113 112 104 109 111 102 096 073 110 067 105 108 099 069 101 063 097 100 071 034 070 094 106 061 083 090 091 010 072 051 057 077 015 087 049 062 054 022 082 068 076 081 085 053 060 007 036

No. of sequencesc

Family

Genus

Closest match

Accession No.

Sequence identity

Leuconostocaceae Leuconostocaceae Lactobacillaceae Leuconostocaceae Leuconostocaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Leuconostocaceae Lactobacillaceae Leuconostocaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Leuconostocaceae Leuconostocaceae Lactobacillaceae Lactobacillaceae Leuconostocaceae Lactobacillaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Lactobacillaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Leuconostocaceae Lactobacillaceae Lactobacillaceae Leuconostocaceae Leuconostocaceae

Weissella Weissella Lactobacillus Leuconostoc Weissella Pediococcus Lactobacillus Pediococcus Lactobacillus Leuconostoc Lactobacillus Leuconostoc Lactobacillus Lactobacillus Lactobacillus Lactobacillus Lactobacillus Lactobacillus Weissella Weissella Lactobacillus Leuconostoc Weissella Pediococcus Leuconostoc Weissella Weissella Weissella Weissella Weissella Lactobacillus Lactobacillus Lactobacillus Lactobacillus Lactobacillus Lactobacillus Leuconostoc Leuconostoc Leuconostoc Leuconostoc Leuconostoc Leuconostoc Leuconostoc Pediococcus Pediococcus Weissella Weissella

Weissella soli Weissella sp. Lactobacillus spp.d Leuconostoc pseudomesenteroides Weissella sp. Pediococcus pentosaceus Lactobacillus sp. Pediococcus parvulus Lactobacillus oligofermentans Leuconostoc carnosum Lactobacillus iners clone Leuconostoc palmae Lactobacillus hammesii Lactobacillus coryniformis Lactobacillus (para)casei Lactobacillus sp. Lactobacillus sp. Lactobacillus sp. Weissella beninensis Weissella sp. Lactobacillus sp. Leuconostoc sp. Weissella sp. Pediococcus argentinicus Leuconostoc sp. Weissella beninensis Weissella beninensis Weissella soli Weissella sp. Weissella spp.d Lactobacillus concavus Lactobacillus oligofermentans Lactobacillus siliginis Lactobacillus sp. Lactobacillus spp.d Lactobacillus spp.d Leuconostoc carnosum Leuconostoc citreum Leuconostoc lactis Leuconostoc mesenteroides Leuconostoc sp. Leuconostoc sp. Leuconostoc sp. Pediococcus inopinatus Pediococcus inopinatus Weissella sp. Weissella sp.

GU470977.1 JX826529.1 AB671287.1 JX866706.1 JQ726614.1 JX679020.1 JX826575.1 JQ249066.1 AY733084.2 CP003851.1 AY283272.1 AM940225.1 AB512777.1 JQ249069.1 JQ446490.1 AB682644.1 HE616585.2 AY681130.1 EU439435.2 JQ726614.1 JF345714.1 JQ286951.1 JQ726614.1 NR_042623.1 JN128638.1 EU439435.2 EU439435.2 GU470977.1 JQ726614.1 AB690344.1 AB682360.1 AY733084.2 AB681447.1 HM534767.1 JQ043378.1 AY733084.2 CP003851.1 JX490162.1 JN573615.1 HE962112.1 JX067698.1 JX026037.1 JX067698.1 JX397935.1 JX397935.1 JQ726614.1 JQ726614.1

100.0 100.0 100.0 100.0 99.7 100.0 100.0 100.0 99.0 100.0 100.0 95.9 100.0 98.7 100.0 100.0 100.0 99.7 94.6 98.4 99.7 95.9 96.8 97.5 95.6 98.4 99.1 91.6 97.5 95.9 98.4 95.2 100.0 99.7 99.7 97.5 94.3 98.7 96.5 98.7 98.1 97.8 97.8 97.8 97.8 97.8 97.8

9548 8287 4908 3555 3367 1129 824 584 341 316 304 108 104 81 33 26 20 9 7 6 4 4 4 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

Bacteria were grouped into OTUs based on a 3% sequence dissimilarity cut-off at the 16S rRNA gene (on average 317 bp). Based on BLAST analysis (November 2012). In total 33,624 LAB sequences were obtained (singletons excluded). Several species showed the same sequence identity percentage.

total covering about 63% of all sequences obtained) and seven other OTUs (OTU 007, 034, 036, 072, 090, 091 and 106) which were represented by only a few sequences (Table 1). Lactobacillus was found as the second most dominant genus (20% of all sequences), followed by Leuconostoc (11% of all sequences). Huge differences were, however, found between the LAB communities from samples from 2010 and samples from 2011 (Fig. 4). For example, the genus Lactobacillus was more abundant in 2010 than in 2011. Starting with relatively fewer amounts in barley harvested in 2011, relative abundance of Lactobacillus dropped seriously compared to 2010. Further, Weissella members were relatively more abundant in 2011, reaching a quite consistent high relative abundance throughout the whole process (average of 82%). Interestingly, different Weisella OTUs were found to dominate in the two years investigated. More specifically, whereas OTU 113 dominated in 2010, OTU 112 was by far the most dominant in 2011 (Fig. 4). Finally, malting the same

batch of barley in two different malting houses resulted in a dominance of different genera during germination in 2010: Lactobacillus dominated during germination in malting house M1, while Weissella dominated in malting house M2. In addition, Pediococcus was almost exclusively detected in malting house M2 in 2010, where they represented 11.2% of the LAB community after 1 day of germination, 4.6% after 5 days of germination, and 12.3% in malt. In the barley itself, these Pediococcus bacteria represented 32.6% in 2010 (Fig. 4). In 2011, Pediococcus bacteria were less abundant and represented only 1.6% of the LAB bacteria in barley and about 0.1% for the different malting steps (Fig. 4). In contrast to T-RFLP, cluster analysis clearly separated samples from the different harvest years (Fig. 2C). Process samples from malting house M1 and M2 clustered separately in 2010, except M_0_M2. In 2011, all samples, with exception of the barley sample, were remarkably similar with at least 94% similarity. The


discrepancy between both techniques applied in this study may be explained by the more sensitive and more accurate nature of 454 pyrosequencing to describe microbial communities (Gilbert et al., 2009), as can also be illustrated by the number of LAB-associated TRFs (#30) and OTUs (#47) found. Another difference between both techniques was observed regarding detection of Leuconostoc bacteria. Whereas these bacteria were found as the dominant LAB (17e60% range between all samples) using T-RFLP, they only represented 1e23% of the 454 sequence dataset in each sample. Presumably, this may be explained by the base composition of primer 341F, which was used for the 454 pyrosequencing analysis and not for T-RFLP, and has one mismatch at base 14 for Leuconostoc species. Interestingly, significant differences between the LAB community structure during malting were observed with 454 pyrosequencing, starting from one and the same barley batch processed in different malting houses (see also Fig. 2C). The genus Pediococcus, for example, was only detected in malting house M2 which has open germination rooms. Pediococcus has been linked to a better malt quality by Laitila et al. (2006a): inoculation favoured yeast growth during malting and restricted the growth of harmful bacteria and Fusarium fungi. Moreover, malt characteristics were enhanced, including reduction in wort viscosity and b-glucan content and enhanced xylanase and microbial b-glucanase activities. Finally, improved lautering performance was observed. At the same time, malting house M2 consistently results in malt with higher friabilities and lower Partly Unmodified Grains (PUG) and

45

Whole Unmodified Grains (WUG) values, which is linked to higher malt quality (data not shown). It remains to be investigated, however, whether Pediococcus has a particular role in this observation. 4. Conclusions The entire bacterial community composition of barley being industrially malted (variety Sebastian; French harvest) was relatively limited but changed along the process as observed with TRFLP. These shifts in microbial community structure can probably be explained by the changing incubation conditions during the process. Zooming in on the LAB community with 454 pyrosequencing also revealed a limited diversity with five OTUs that were dominantly present in all samples, representing members of Weissella, Lactobacillus and Leuconostoc. 454 pyrosequencing of the LAB community revealed a different community structure for both investigated harvest years, while T-RFLP did not detect this difference. Moreover, 454 sequencing showed an impact of malting the same barley batch in different malting systems, resulting in a significantly different LAB community structure. The higher resolution obtained with 454 can be explained by the more robust nature of 454 pyrosequencing to thoroughly study population dynamics. We hypothesize that the differential microflora between both malting houses in 2010, e.g. represented by Pediococcus species, may lead to a higher friability which therefore results in a higher malt quality. More research is needed to clarify the role of these differential microflora in malt quality. Both from the T-RFLP and 454 analysis a strong influence could be seen from the microflora initially present in the barley, suggesting an important role for field and storage microflora in the malting process, irrespective of the malting conditions. Nevertheless, microflora relative abundances are determined by those malting process parameters. Together with increased knowledge about the functions and steering of these microorganisms, this study may lead to enhanced process control and production of high quality malt. Acknowledgements We are grateful to Marijke Lenaerts for skilled technical assistance. In addition, we thank Albert Maltings for supplying samples. The research was funded by the Institute for the Promotion of Innovation by Science and Technology in Flanders (IWT grant OZM095133). Appendix A. Supplementary material Supplementary data related to this article can be found online at http://dx.doi.org/10.1016/j.fm.2013.10.010. References

Fig. 4. Relative abundance and distribution of the most dominant lactic acid bacteria during industrial malting of the French Sebastian barley variety harvested in (A) 2010 and (B) 2011, based on 454 16S rRNA pyrosequencing. Only OTUs that represented more than 0.1% of the sequences obtained in this study are specifically indicated; other OTUs are grouped together in “other OTUs”. Samples are annotated by the nature of the sample (B ¼ barley; G1d ¼ 1 day germinated barley; G5d ¼ 5 days germinated barley; M ¼ malt), harvest and malting year (0 ¼ 2010; 1 ¼ 2011) and malting house (M1 and M2).

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