microbial communities from 20 different hydrogen-producing reactors studied by 454 pyrosequencing

BIOENERGYAND BIOFUELS

Microbial communities from 20 different hydrogen-producingreactors studied by 454 pyrosequencing

Claudia Etchebehere1 & Elena Castell2 & Jorge Wenzel1 & Mlida del Pilar Anzola-Rojas3 &Liliana Borzacconi2 & Germn Buitrn4 & Lea Cabrol5 & Vivian Mara Carminato3 &Julian Carrillo-Reyes4,6 & Crhistian Cisneros-Prez6 & Laura Fuentes1 &Ivn Moreno-Andrade4 & Elas Razo-Flores6 & Gonzalo Ruiz Filippi5 &Estela Tapia-Venegas5 & Javiera Toledo-Alarcn5 & Marcelo Zaiat3

Received: 14 September 2015 /Revised: 12 January 2016 /Accepted: 14 January 2016# Springer-Verlag Berlin Heidelberg 2016

Abstract To provide new insight into the dark fermentationprocess, a multi-lateral study was performed to study the mi-crobiology of 20 different lab-scale bioreactors operated infour different countries (Brazil, Chile, Mexico, andUruguay). Samples (29) were collected from bioreactors withdifferent configurations, operation conditions, and perfor-mances. The microbial communities were analyzed using16S rRNA genes 454 pyrosequencing. The results showednotably uneven communities with a high predominance of aparticular genus. The phylum Firmicutes predominated inmost of the samples, but the phyla Thermotogae orProteobacteria dominated in a few samples. Genera fromthree physiological groups were detected: high-yield hydro-gen producers (Clostridium, Kosmotoga, Enterobacter), fer-menters wi th low-hydrogen yie ld (mos t ly f rom

Veillonelaceae), and competitors (Lactobacillus). Inocula, re-actor configurations, and substrates influence the microbialcommunities. This is the first joint effort that evaluateshydrogen-producing reactors and operational conditions fromdifferent countries and contributes to understand the dark fer-mentation process.

Keywords Biohydrogen . Dark fermentation . Reactor .

Microbial community . 454 pyrosequencing

Introduction

Hydrogen is a new clean energy carrier that can be producedvia anaerobic digestion during waste treatment. To produce

Electronic supplementary material The online version of this article(doi:10.1007/s00253-016-7325-y) contains supplementary material,which is available to authorized users.

* Claudia [email protected]; http://www.iibce.edu.uy

1 Microbial Ecology Laboratory, BioGem Department, BiologicalResearch Institute Clemente Estable, Ministery of Education, Av.Italia, 3318 Montevideo, Uruguay

2 BioProA Laboratory, Chemical Engineering Institute, EngineeringFaculty, University of the Republic, Herrera y Reissing,565 Montevideo, Uruguay

3 Biological Processes Laboratory, Center for Research, Developmentand Innovation in Environmental Engineering, So Carlos School ofEngineering (EESC), University of So Paulo (USP), EngenhariaAmbientalBloco 4-F, Av. Joo Dagnone, 1100Santa Angelina13.563-120, So Carlos, SP, Brazil

4 Laboratory for Research on Advanced Processes for WaterTreatment, Unidad Acadmica Juriquilla, Instituto de Ingeniera,Universidad Nacional Autnoma de Mxico, Blvd. Juriquilla 3001,76320 Quertaro, Mexico

5 Environmental Biotechnology Laboratory, Escuela de IngenieraBioqumica, Pontificia Universidad Catlica de Valparaso, Chile,Avenida Brasil, 2085 Valparaso, Chile

6 Divisin de Ciencias Ambientales, Instituto Potosino deInvestigacin Cientfica y Tecnolgica A.C. (IPICYT), Camino a laPresa San Jos 2055, Lomas 4. Seccin, 78216 San Luis Potos, SLP,Mexico

Appl Microbiol BiotechnolDOI 10.1007/s00253-016-7325-y

hydrogen, it is necessary to inhibit the hydrogen-consumingprocesses (methanogenesis and homoacetogenesis) and selectthe hydrogen-producing fermentation processes. To this end,several sources of inocula (anaerobic sludge, aerobic sludge,sediments, compost) and different pretreatment methods (ther-mal shock, addition of acid or base, addition of inhibitors orbiokinetic control of the population) have been applied(Mohammadi et al. 2011). Different inoculum sources are as-sociated with specific microbial communities, which in turnare associated with certain metabolic pathways that generatevarying yields (Chang et al. 2011; Ren et al. 2008). A detailedstudy comparing different inocula and pretreatments showedan effect on the hydrogen production and the bacterial com-munity fingerprint (via 16S rRNA gene amplification in de-naturing gradient gel electrophoresis) (Penteado et al. 2013).However, the composition of the microbial community alsodepends on such operational conditions as substrate (Akutsuet al. 2009) and reactor configuration (Wu et al. 2008), amongothers. With respect to the substrate, different substances suchas fructose, glucose, sucrose, maltose, cellobiose, andmaltotriose affect the hydrogen- producing community, asevaluated in functional hydrogenase genes analysis(Qumneur et al. 2011). The organic loading rate influencesthe yield and the obtained production, but its influence on themicrobial population is not clear (Dvila-Vzquez et al. 2009;Kraemer and Bagley 2007; Lee et al. 2014).

Among the dark fermentation hydrogen-producing bacteria,organisms from the Clostridium genus and Enterobacteriaceaefamily are the most widely reported in the mesophilic category,whereas Thermoanaerobacteriales are frequently reported inthermophilic conditions. Within the groups of competitors, mi-croorganisms with different fermentative metabolic pathways,e.g., lactic acid and propionic fermentation, have been de-scribed (Hung et al. 2011). Two microbiological processescould be linked to consumption of produced hydrogen, i.e.,hydrogenotrophic methanogenesis and homoacetogenesis,which might cause instability in hydrogen production (Saady2013).

Successful biological hydrogen production depends on theoverall performance of the bacterial communities selected inthe bioreactors, and knowledge of the microbial communitycould provide new insights into the process and aid in controlof its stability. Moreover, the metabolic pathways of a singlemicroorganism can change during operation according to exter-nal conditions (Lu et al. 2016). Understanding the relationshipsbetween variations in microbial composition, metabolic path-ways, and hydrogen production efficiency is the first step inconstructing more efficient hydrogen-producing consortia(Tolvanen and Karp 2011).

Numerous methods are available to characterize microbialcommunities, e.g., denaturing gradient gel electrophoresis(DGGE), cloning-sequencing, terminal restriction fragmentlength polymorphism (T-RFLP), fluorescence in situ

hybridization (FISH), and quantitative polymerase change re-action (q-PCR), among others. More recently, the next-generation sequencing methods present an interesting alterna-tive because they are rapid and low-cost and offer high cover-age of the communities (Tolvanen and Karp 2011). Using thisnew technology, important studies were performed to examinethe microbiology of different wastewater treatment reactors,including full-scale methanogenic reactors (Werner et al.2011, De Vrieze et al. 2015) and activated sludge reactors(Zhang et al. 2012). With the correlations between microbialcommunity compositions and reactor operation conditions andperformance, valuable information could be obtained for use inmanaging the reactors. Deep sequencing technology was alsoapplied to describe the microbial communities present in singlehydrogen-producing reactors operated under different condi-tions (Laothanachareon et al. 2014, Ferrz et al. 2015). Theseworks were focused in describing the microbiology of a singleprocess and not in the comparison of the microbiology from aset of different hydrogen-producing reactors. The similaritiesand differences in the hydrogen-producing reactormicrobiomes, and the environmental/process parameters thatdrive these differences, remain poorly understood.

In an effort to understand the microbiology of the hydrogenproduction process via dark fermentation, an internationalstudy was conducted to evaluate the microbiology of lab-scale bioreactors operated under different conditions, whichcreated different performance outputs. The aims of this workwere to compare the microbial population selected in the dif-ferent hydrogen-producing reactors and to determine the in-fluences of the inocula, the reactor configurations, and theoperation conditions in the populations established. To per-form this work, 29 samples were collected from 20 differentreactors, which were previously operated in five different re-search labs. The microbial populations were studied using 454pyrosequencing of 16S rRNA genes and associating their eco-logical distance and operational parameters through multivar-iate statistical analysis.

Materials and methods

Reactors and sampling

All of the reactors studied were lab-scale reactors operated forproduction of hydrogen in five different laboratories located infour different countries (Brazil, Chile, Mexico, and Uruguay).The reactors represented different configurations: fixed bedreactors (FBR), expanded granular sludge bed (EGSB),upflow anaerobic sludge bed (UASB), sequencing batch reac-tors (SBR), and continuous stirred tank reactors (CSTR). Theoperation conditions and hydrogen production results of thereactors at the time of sample collection are presented inTable 1. Information about the time when the samples were

Appl Microbiol Biotechnol

taken and the references where more information can be foundabout the different reactors performances are included in thesupplementary material (Table S1). Different inocula sourcesand different pretreatment methods were used in the differentreactors (Table 2). Samples were collected from the biomass

of the reactors and homogenized, and the cells were separatedby centrifugation (5 min at 6000 g). For the reactors withbiomass attached to an inert support (FBR), a previous stepwas performed in which the cells were separated from thesupport. Biomass detachment from the support was performed

Table 1 Characteristics of the reactors sampled, the hydrogen yield, and the volumetric hydrogen production rate (VHPR) of the reactor when thesamples were taken are also shown

Country (sample) Reactorsa HRT (h) Substrate OLRj

(g COD/L/day)SOLRk

(g COD/gVS/day)H2 yield(mmol H2/g COD)

VHPR12

(LH2/Ld)

Brazil (B1) FBRc (25 C)b 2 Sucrose 24 4.99 1.10 0.6

Brazil (B2) FBRd (25 C) 2 24 8.22 0.37 0.3

Brazil (B3) FBRe (25 C) 2 24 8.53 2.21 1.4

Brazil (B4) FBRf (25 C) 2 24 4.68 1.10 0.8

Chile (C1) CSTR (37 C) 12 Glucose 20 19.61 5.92 2.8

Chile (C2) CSTR (37 C) 12 Glycerol 10 16.58 5.92 2.8





Mexico (M1) FBRc (25 C) 3 Glucose 24 2.40 6.25 3.4

Mexico (M2) 3 36 3.00 6.69 5.3

Mexico (M3) 3 48 3.43 5.80 6.4

Mexico (M4) 3 60 3.75 1.78 2.5

Mexico (M5) EGSB (30 C) 10 24 2.40 6.69 3.7

Mexico (M6) 10 36 2.57 4.02 3.1

Mexico (M7) EGSB (30 C) 10 36 3.60 4.02 3.2

Mexico (M8) UASBg (35 C) 5,5 7 1.10 3.12 2.1

Mexico (M9) UASBdh (35 C) 6 5 0.68 5.00 3.1

Mexico (M10) UASBi (35 C) 5.5 5 0.63 15.17 1.7Mexico (M11)

Mexico (M12) SBR (35 C) 24 Organic solid waste 5 N.D. 5.00 0.8

Mexico (M13) 72 2 N.D. 3.12 0.2

Uruguay (U1) UASB (27 C) 16 Raw cheese whey 10 1.00 4.46 1.0

Uruguay (U2) 22 20 2.00 1.34 0.6

Uruguay (U3) 24 30 3.00 4.02 2.6

Uruguay (U4) 24 34 3.40 0.13 0.1

Uruguay (U5) FBR (30 C) 24 Cheese whey powder 22 2.20 0.45 0.2

Uruguay (U6) 27 37 3.70 1.34 1.1

N.D. not determineda Reactor configuration: CSTR, completely stirred tank reactor; FBR, fixed bed reactor; EGSB, expanded granular sludge bed; UASB, upflow anaerobicsludge bed; SBR, sequencing batch reactorsb Operation temperaturec Polyurethane foam, packing materiald Ceramic, packing materiale Low-density polyethylene packing material (Ca added, 3.61 mg/L)f Low-density polyethylene packing material (Ca added, 2.45 mg/L)g Continuous operation coupled with a membranehDiscontinuous operation (UASBd)i Continuous operation, M10 and M11 samples were taken at operation days 65 and 67, respectivelyj Organic loading ratek Specific organic loading rate.


via ultrasonic treatment (40 W for 5 min) (5 min at 6000 g)followed by centrifugation. All samples were stored at 20 Cuntil further use.

DNA extraction and 454 pyrosequencing of 16S rRNAgenes

Subsamples (1 g) were collected from the biomass of the re-actors, and genomic DNAwas extracted using the PowerSoilDNA Kit (Mo Bio laboratories, Carlsbad, CA, USA) accord-ing to the manufacturers instructions. After checking thequality of the DNA extracted via gel electrophoresis, theDNA was dehydrated with 95 % ethanol and submitted to

the Institute for Agrobiology Rosario (INDEAR, Rosario,Argentina), where pyrosequencing analysis was performedusing a Roche Genome Sequencer FLX Titanium system.The 16S rRNA genes were amplified with the primer sets515 forward and 806 reverse (Caporaso et al. 2011).

Analysis of 454 pyrosequencing results

Sequences were analyzed using the Quantitative Insights intoMicrobial Ecology (QIIME) software (Caporaso et al. 2010).Chimera detection was performed with USEARCH 6.1 soft-ware (Edgar 2010). De novo operational taxonomic units(OTUs) picking and assignment were defined using theUClust algorithm based on 97 % sequence identity with theGreengenes database reference sequences (DeSantis et al.2006). A randomly subsampled OTU table (an OTU assign-ment matrix with abundances) with an equal depth of 6000randomly selected reads per sample was generated withQIIME BIOM extension file management tools. Sequencealignments were performed with PyNAST. Rarefied alpha di-versity analysis and descriptive community composition fig-ures via alpha and beta diversity analysis were performed withthe QIIME recommended pipeline workflow. The sequenceswere deposited in the database of the National Center forBiotechnology Information (NCBI) as bioproject numberPRJNA263568.

Multivariate and statistical analysis

To evaluate the ecological distances between communitiesand the effect of the applied conditions, canonical corre-spondence analysis (CCA) was performed by consideringthe OTU assignment matrix obtained in the sequenceanalyses and the operational conditions as constrainedvariables, i.e., inoculum source, substrate, reactor config-uration, and organic loading rate (OLR). Other perfor-mance parameters, such as molar yield (mmol H2/gCOD), specific production rate (mol H2/g VS/day), andthe volumetric production rate (mL H2/L/day), were fittedas environmental vectors in the ordination graph. All sta-tistical analyses were performed in R (v 3.1.2) using theBvegan^ package (Oksanen et al. 2012).

To consider the impact of the substrate in sample C, a PCAanalysis was performed only with these samples; for that, thesoftware package PASTwas used (Hammer et al. 2001). Boxplots of reactor performance output dataset and relative abun-dance of predominant groups of microorganisms were alsoperformed using the PAST software package. In the box plots,the 2575% quartiles are drawn using a box and the median isshown with a horizontal line inside the box, the outlier valueswere also indicated.

Table 2 Origin of the inoculum and pretreatment of the biomass usedto seed each reactor

Country (Sample) Inoculum Treatment

Brazil (B1) Indigenous None




Chile (C1) Anaerobic sludge Dry heat treatment,24 h 105 C

Chile (C2) Anaerobic sludge Washout

Chile (C3) Activated sludge Washout

Chile (C4) Anaerobic sludge Washout

Chile (C5) Anaerobic sludge Controlled aeration

Chile (C6) Anaerobic sludge Controlled aeration

Mexico (M1) Anaerobic sludge Washout




Mexico (M5) Anaerobic sludge Wet heat treatment,boiled 1 h

Mexico (M6) Anaerobic sludge Wet heat treatment,boiled 1 h






Mexico (M12) Anaerobic sludge Dry heat treatment,24 h 105 C

Mexico (M13) Anaerobic sludge Dry heat treatment,24 h 105 C

Uruguay (U1) Compost None




Uruguay (U5) Indigenous None

Uruguay (U6) Indigenous None


Results

More than 360,000 good-quality reads were retrieved from allthe samples with values from 6700 to 30,000 reads by sample.To enable comparison of the samples in an equal depth, sub-sampling was performed, and 6000 reads were selected bysample. The rarefaction curves showed an effect of reactorconfiguration on the diversity; the samples from the UASBand FBR were more diverse than the samples from the CSTRand SBR (Fig. 1).

Classification of the reads

Classification of the reads revealed a high predominance ofthe phylum Firmicutes (Fig. 2). This phylum was detected inall the samples and predominated in 25 of the 29 samplesanalyzed. In the other samples, the phyla Thermotogae orProteobacteria dominated, whereas in sample B1, no domi-nance of a particular phylum was observed.

Classification at the level of genera showed that the com-munities were generally uneven, with a high predominance ofa particular genus. In certain samples, one genus(Selenomonas, Megasphaera) displayed dominance of morethan 75 %, and in other samples, a genus (Clostridium,Selenomonas, Acidaminococcus, Pectinatus, Kosmotoga)had a dominance of greater than 50 % (Fig. 3).

Six genera (Prevotella, Lactobacillus, Clostridium,Selenomonas,Megasphaera, and Enterobacter) were detectedin more than ten samples, indicating that they were particular-ly selected in the reactor configurations used.

Methanogens are one of the potential hydrogen consumers,which belong to the phylum Euryarchaeota. This phylumwas

detected in seven samples with a dominance of less than 10%,most of the genera detected (Methanobacterium andMethanolinea) use hydrogen as a substrate (Table 3).

To reveal if there is a relationship within the abundance ofparticular genera and reactor performances, the samples wereseparated in quartiles according to the distribution of the hydro-gen yield (HY) and volumetric hydrogen production rate(VHPR) values in a boxplot (Figure S2). Three groups of sam-ples were then defined: group 1 (good performance) composedby the samples with the top quartile values (HY higher than2.0 mmol H2/g COD and/or VHPR higher than 6 LH2/Ld),group 3 (low performance) composed with the samples present-ing the low quartiles values (HY lower than 0.8 mmol H2/gCOD and/or VHPR lower than 3 LH2/Ld), group 2 (mediumperformance) composed by the samples with intermediatevalues (Table S2). The relative abundance of the predominantgroups of microorganisms in the three groups of samples (group1, 2, and 3) was plotted as boxplots (Fig. 4). From this figure, itis possible to observe that the samples taken from reactors withgood performance (group 1) presented a high abundance oforganisms previously described as hydrogen-producing generaClostridium andKosmotoga. This tendency was not so clear forEnterobacter, as this genus was abundant in either good- or low-performance groups. On the other hand, the samples with higherabundance of low-hydrogen yield-producing bacteria from theVeillonelaceae family presented in general medium to low yieldand VHPR (groups 2 and 3). Interestingly, the samples withhigh abundance of lactic acid bacteria (from the generaLactobacillus, Sporolactobacillus, and Streptococcus) weregrouped in the good and low performance groups (groups 1and 3).

Multivariate analysis results

Effects of the inoculum source, substrate, reactor configura-tion, and high- or low-specific organic loading rate (SOLR)were observed in the ecological distances between samplesand their grouping according to the CCA (Fig. 5). Threegroups of samples could be observed when considering the

Fig. 1 Rarefaction curves according to reactor configuration. Yellow:UASB discontinuous, violet: reactor UASB, orange: reactor FBR, red:CSTR, blue: EGSB, green: SBR. The bars indicate the range of valuesobtained for each reactor configuration

Fig. 2 Taxonomic profiles of the microbial communities classified at thelevel of phylum according to the analysis of the pyrosequencing results


source of inoculum: anaerobic sludge (pretreated with differ-ent methods), compost, and indigenous inocula (Fig. 5a).According to the substrate, the samples were separated intwo main groups composed by the samples from reactors fedwith defined compounds (glucose, sucrose, and glycerol) andreactors fed with real wastes (Fig. 5b). In order to evaluate theeffect of substrate on the microbial communities from a sim-ilar reactor configuration and operated under similar condi-tions, a PCAwas performed with sample C (Figure S1). Theresults showed that the samples were not grouped according tothe substrate; moreover, samples C5 and C6 which only differin the substrate used were grouped together. The reactor con-figuration also shows an effect in the communities selected;

close ecological distances between those communities devel-oped in UASB and SBR and between EGSB and FBR wereobserved while the CSTR communities were located separate-ly (Fig. 5c).

To consider the effect of the SOLR applied to the reactors,four groups of SOLR values (g COD/g VS/day) were arbi-trarily defined: less than 3, from 3 to 8, from 8 to 19, andhigher than 19. The CCA showed that communities developedin the reactors operated at low SOLR (less than 8 g COD/gVS/day) grouped separated from the samples taken from thereactors operated at higher SOLR values. Even though a clearcommunity distribution was correlated on those reactors op-erated at >19 g COD/g VS/day; however, this could be

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Fig. 3 Comparative relativeabundance profiles of thepredominant genera detected inthe microbial communities fromthe different reactor samples


explained by the reactor configuration, considering that onlyFBR were operated at those SOLR.

Three response variables describing the performance of thehydrogen-producing reactors: molar yield (mmol H2/g COD),

specific production rate (mmol H2/g SV/day), and volumetricproduction rate (L H2/Lreactor/day) were determined for eachreactor at the moment of sampling (Table 1). These valueswere fitted as environmental vectors in the ordination graph

Table 3 Classification of the OTUs in genera with known methanogenic capacity. The samples where these OTUs were detected and the total amountof reads in these samples are shown. The substrate of the genera detected according to bibliography is also indicated

OTUs Family Genus Substrate Total number of reads Detected in samples

OTU252 Methanobacteriaceae Methanobacterium H2 + CO2 804 M8, M10, B1

OTU669 Methanobacteriaceae Methanobacterium H2 + CO2 709 M9, M10, M11, B1, B2, U6

OTU403 Methanobacteriaceae Methanobacterium H2 + CO2 329 M9, M10, M11

OTU343 Methanoregulaceae Methanolinea H2 + CO2 138 M9, M10, M11

OTU821 Methanobacteriaceae Methanobacterium H2 + CO2 133 B1, B2, B4

OTU83 Methanosaetaceae Methanosaeta Acetate 82 M10, M11

OTU787 Methanobacteriaceae Methanobrevibacter H2 + CO2 42 C4, U2

OTU264 Methanosaetaceae Methanosaeta Acetate 11 B1

OTU688 Methanosarcinaceae Methanosarcina Acetate; H2 + CO2 11 B1

OTU742 Methanomassiliicoccaceae Unclassified H2 + CO2 9 M10

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Fig 4 Links between abundance of the predominant microorganisms andreactor performances. The samples were grouped according to the valuesof HY and VHPR in three groups: group 1 (good performance) iscomposed by the samples with HY higher than 2.0 mmol H2/g CODand/or VHPR higher than 6 LH2/Ld, group 3 (low performance) is

composed with the samples with HY lower than 0.8 mmol H2/g CODand/or VHPR lower than 3 LH2/Ld, group 2 (medium performance) iscomposed by the samples with intermediate values. The boxplots showthe relative abundance of predominant groups of microorganisms in threegroups of samples (1, 2, and 3)


in the CCA analysis. The three variables presented a positivecorrelation with the communities from the reactors inoculatedwith anaerobic sludge, which were previously treated bywashout, heat, or control aeration (Fig. 5a). Positive correla-tions were also found between microbial communities fromthe reactors fed with glucose and their volumetric productionrate (Fig. 5b), the communities from CSTRs and their specifichydrogenogenic activity (Fig. 5c).

Discussion

In spite of the different operation conditions applied andthe different reactor configurations, a similar trend wasobserved: the communities selected were very specialized,

presenting low diversity and high predominance of a par-ticular genus. The extreme operation conditions applied tothis kind of reactors (low pH, high OLR, low HRT) couldbe the reason for the selection of a very specialized com-munity. The high dominance of a particular organismcould be favorable for the process if the correct organismis selected (in this case, organisms with high hydrogenyield), but, on the other hand, the community unevennesscould be one of the reasons for the low stability of theprocess.

The phylum Firmicutes predominated in most of the sam-ples, this dominance was also observed by other authors inhydrogen-producing reactors (Si et al. 2015). It has to be takeninto account that the genus Clostridium belonged to the phy-lum Firmicutes, and several species from this genus are

Fig. 5 Canonical correspondent analysis (CCA) performed byconsidering the OTU assignment matrix obtained in the pyrosequencinganalyses and the operational conditions as constrained variables. CCAconstrained to the source of inoculum (a), the substrate (b), the reactor

configuration (c), and the SOLR (d). The ellipses predict the position ofthe communities developed according the different categories evaluated.Discontinuous UASB reactor is pointed out in c.


reported to present the highest hydrogen yields in pure cul-tures (Zhao et al. 2011; Hu et al. 2014).

According to the bibliography (Holt et al. 1994), the generadetected in the reactors can be grouped in three physiologicalguilds: (1) genera that harbor species with a high capacity forhydrogen production (Clostridium, Kosmotoga, Enterobacter),(2) genera from organisms that exhibit fermentative metabolicpathways with low hydrogen yield (Selenomonas, Pectinatus,Megasphaera, Prevotella, Acidaminococcus), (3) genera withno capacity for hydrogen production (competitors), such as thelactic acid bacteria (Lactobacillus).

Genera with high capacity for hydrogen production

The presence of organisms from the genera Clostridium andEnterobacter is expected in hydrogen-producing systems be-cause these groups harbor many species with the ability toproduce hydrogen and are considered to be the organisms thatdrive hydrogen production via dark fermentation (Wang andWan 2009). The genus Kosmotoga was not frequently report-ed in hydrogen-producing reactors. Organisms from this ge-nus are able to degrade different sugars, i.e., certain organicacids and glycerol, and they produce hydrogen, CO2, andacetate as their main products (Bhandari and Gupta 2014;LHaridon et al. 2014). Although these organisms are thermo-philic, they also can grow at mesophilic temperatures. In par-ticular, Kosmotoga olearia has a temperature range of 2080 C, suggesting that they can grow at the operational tem-peratures of the reactors (DiPippo et al. 2009). The genusKosmotoga was detected in high abundance (greater than75 %) in two samples (M8 and M9). We cannot find anyparticular reason for the selection of this genus in these par-ticular reactors; it is likely that these organisms were present inthe inoculum and persisted during operation. The presence ofKosmotoga was recently reported in a metagenomic study offour full-scale mesophilic anaerobic digesters (Yang et al.2014). The role and importance of this organism inhydrogen-producing reactors must be studied further.

It is well reported that many species from the Clostridiumgenus presented high hydrogen yield (Schleifer 2009), this isin accordance to our findings, as the relative abundance ofClostridium is high in the samples with good hydrogen per-formance (group 1) (Fig. 4). However, the same trend was notobserved for Enterobacter genus. As the metabolic pathwaysto obtain hydrogen are different in the two genera, this couldbe the explanation of the lower performance of Enterobacter-rich samples. The theoretical yield reported for members ofEnterobacter species are in general lower than that reportedfor Clostridium species (Gottschalk 1986). Nevertheless, asorganisms from Enterobacter genus are facultative anaerobicorganisms, they could help to maintain the anaerobic condi-tions required for other strict anaerobic organisms in the reac-tors as was shown by Yokoi et al. 1998.

Genera with low capacity for hydrogen production

An interesting finding is the predominance of organisms fromthe Veillonellaceae family. Several microorganisms belongingto this strict anaerobic bacteria family were detected in thereactors operated under different conditions. Similarly, the re-vision performed by Hung et al. (2011) stated that organismsfrom this family were reported in hydrogen-producing reac-tors all over the world. Organisms from the familyVeillonellaceae are reported to tolerate high concentrationsof organic acids, and this could be an explanation for theirselection in hydrogen-producing reactors. Moreover, most ofthe species from the genus Megasphaera, which displayedhigh abundance in many samples, do not consume sugarsand have the ability to consume organic acids with the pro-duction of small amounts of hydrogen (Schleifer 2009; Wonet al. 2013). This metabolic pathway could increase the pro-duction of hydrogen after sugar consumption. In this case,although these organisms presented low hydrogen yields, theycould be considered as helpers in the hydrogen production.However, the relative abundance of the Veillonelaceae familyis higher in samples with medium to low reactor performance,this suggests that the presence of these organisms could notonly help in the stability of the process but they may also beresponsible of the low yield detected in some reactors.Understanding the relationship between the organisms withhigh and low hydrogen yields could be the key to increasinghydrogen production in these reactors.

Within the Veillonelaceae family, the genus Selenomonaswas especially abundant in samples from reactors fed withcheese whey (samples U) (Fig. 3), the presence of this organ-ism was also reported previously in other hydrogen-producingreactors fed with the same substrate (Castell et al. 2009, Rosaet al. 2014a), suggesting that this organism was specially se-lected with this substrate. It was reported that Selenomonas sp.has the capability for hydrogen fermentation using glucose orlactate as substrate (Luo et al. 2008). This ability could be thereason for this selection, as lactic acid is a component fre-quently found in the cheese whey produced during cheesefermentation by lactic acid bacteria (Castell et al. 2011).

Genera that compete with hydrogen-producing organisms

Lactic acid bacteria were the main competitors detected in thereactors; they were reported to decrease the hydrogen produc-tion via competition for the substrate and via inhibition causedby production of high amounts of lactic acid, low pH, andantimicrobial peptides known as bacteriocins (Noike et al.2002). However, in our work, lactic acid bacteria were detect-ed in co-dominance with organisms from the Clostridium ge-nus in samples with high production of hydrogen (samplesM1, M2, and M3). Moreover, samples with high relativeabundance of lactic acid bacteria presented high and low


hydrogen performance (Fig. 3). According to these results, itcould be inferred that inhibition does not always occur orcould be avoided in certain conditions. Similarly, Morraet al. (2014) reported the coexistence of lactic acid bacteriawith various Clostridium species in a pilot-scale hydrogen-producing reactor. These authors suggested that the bacterio-cins potentially produced by lactic acid bacteria inhibit onlycertain Clostridium species and might act to select the specificClostridium strains within the consortium.

Hydrogen-consuming organisms

Two processes are usually linked to the consumption of mo-lecular hydrogen in the reactors: homoacetogenesis andmethanogenesis (Saady 2013). To determine the prevalenceof these pathways in the reactors, we carefully examined thereads classified within genera involved in these two processes.

Homoacetogens are strict anaerobes that are fast growing,phylogenetically diverse, and trophically quite versatile.Homoacetogenic bacteria belonging to Acetobacterium,Butyribacterium, Clostridium, Eubacterium, Peptostreptococcus, and Sporomusa were widely reported (Saady2013). From these genera, we were able to detect a high abun-dance of only Clostridium in the samples analyzed (Fig. 3),but a few species from this genus were described ashomoacetogens (Saady 2013). As the 16S rRNA gene se-quences retrieved in this work were short (235253 nucleo-tides), the classification at the level of species is not accurateand then a clear conclusion about the presence of Clostridiumspecies with reported capacity to perform homoacetogenesiscould not be taken. Moreover, the feasibility of hydrogen pro-duction or consumption by a population of Clostridiummem-bers will also depend on the metabolic state and the environ-ment. Organisms from the Clostridium genus have a versatilemetabolism, and the production or consumption of hydrogenis linked to the central energy metabolism. The redox state(NADH or ATP levels) may regulate secondary metabolismreaction as observed in the change of metabolite profile pro-duced by strains of organisms from this genus during growth(Chandrasekhar et al. 2015, Lu et al. 2016). Therefore, addi-tional efforts are necessary to understand this complex regu-lation and to reveal the prevalence of homoacetogenesis in thehydrogen-producing reactors.

Genera from methanogens (Methanobacterium andMethanolinea), with known hydrogenotrophic capability ac-cording to bibliography (Demirel and Scherer, 2008), weredetected in seven samples (Table 3). Organisms from the ge-nus Methanobacterium were also detected by Carrillo-Reyeset al. (2014), who studied methanogens in hydrogen- produc-ing reactors, and adaptation of organisms from this genus tolow pH was previously demonstrated (Steinberg and Regan2008). Moreover, acidophilic strains belonging to theMethanobacterium genus were described (Methano

bacterium spanolae, Methanobacterium paludis (Patel et al.1990), thus confirming that organisms from this genus areable to produce methane at low pH.

Methanogens were detected in less than 10 % of relativeabundance (Table 3). We were not able to find in the bibliogra-phy values from other hydrogen-producing reactors to make acomparison. In the few works in which the presence ofmethanogens in hydrogen-producing reactors were reported,the authors used specific primers directed to Archaea forDGGE analysis (Carrillo-Reyes et al. 2014; Rosa et al.2014b) and then, their relative abundance in the total commu-nity could not be known. Nevertheless, the abundance of me-thanogenic Archaea reported in the biomass from active me-thanogenic reactors presented similar values. Using ametagenomic approach with no PCR and primers bias, Yanget al. (2014) found that 2.814.64 % of the sequences wereannotated as Archaea in two anaerobic digesters. Pervin et al.2013 found up to 10 % abundance of Archaea also in an an-aerobic digester using gene amplicons generated by PCR withuniversal primers 926F and 1392R. But, when a meta-transcriptomic approach was performed, the abundance ofArchaea transcript genes in an anaerobic digester reachesvalues up to 24 % (Zakrzewski et al., 2012). Then, this lowproportion of Archaea detected in the DNA could be very ac-tive. As we use a DNA approach, we cannot know if theArchaea detected in the hydrogen-producing reactors were ac-tive or were inhibited and then, more work is necessary toaddress this question.

In spite that either hydrogen or acetate is abundant in thehydrogen-producing reactors, the relative abundance ofhydrogenotrophic methanogens is markedly higher than theacetoclastic ones (Table 3). This is in accordance with the differ-ent inhibition sensitivities reported for both groups ofmethanogens, as acetoclasitic methanogens presented lower tol-erances to inhibitors as organic acids or low pH (Hao et al. 2012).

The samples in which these methanogens were detectedwere collected from different hydrogen-producing reactors thatoperated at low pH (values between 4.5 and 5.6) and with highconcentrations of organic acids (data not shown), revealing thatthe methanogens can survive under these extreme conditions.The reactors with biomass retention (UASB, FBR) presented ahigher relative abundance of methanogens than the reactorswith suspended biomass (CSTR), suggesting that cell aggrega-tion might favor the persistence of these organisms. Therefore,because archaea are slower growing than bacteria, growth rateselection would be a suitable treatment for removal of archaea.

Influence of the inoculum, substrate, reactorconfiguration, and operational conditions in the microbialcommunities

The origin of inoculum clearly affected the microbial compo-sition of the biomass. A clear clustering of samples with


anaerobic sludge, compost, and indigenous inocula isobserved. Akutsu et al. (2009) also observed that the originof inoculum has an important influence on hydrogen produc-tion for some substrates. However, these authors did not find aclear relationship between the use of anaerobic sludge as in-oculum and the achievement of higher hydrogen production.We found a correlation between the best performance in termsof volume, specific production rate, and molar yield and thereactors inoculated with anaerobic sludge which were previ-ously treated by washout, heat, and control aeration. Similarresults were obtained by Penteado et al. (2013), who alsoevaluated indigenous inoculum (autofermented); neverthe-less, these authors found a similar molar yield between bothinocula (anaerobic sludge and indigenous), which differs fromour multivariate analysis results. In most of the samples inoc-ulated with anaerobic sludge, the Clostridium genus was dom-inant or subdominant with relative abundance ranging from 10to 75 %, except for those reactors fed with solid organic wastes(Table 2 and Fig. 3), highlighting the relevance of this genus inhydrogen production. In these experiments, different pretreat-ments were applied to the inocula in order to suppress the me-thanogenic activity. However, other strategies for the suppres-sion of the methanogenic activity have been reported includingthe use of a low retention time (Zhang et al. 2006), the pHcontrol (Zhou and Ren 2007), or the combination of low HRTand pH of 5.5 (Hernndez-Mendoza et al. 2014a, b). The resultsobtained showed that the pretreatment of anaerobic sludge isrelated to the best performance of the biohydrogen reactors.Other strategies to obtain hydrogen can also be considered asproducing microorganisms in bioreactors without an inoculumpretreatment and biokinetic control for washout of methano-genic archaea (Zhang et al. 2006, Hernndez-Mendoza et al.2014a, b; Zaiat et al. 2016). It has been reported that thebiokinetic control strategy favors the dominance ofClostridiales and Escherichia coli (Hernndez-Mendoza et al.2014a, b) and Ethanoligenens and saccharolytic Clostridium(Zaiat et al. 2016). When no pretreatment is used, the presenceof other genus which are not hydrogen producers such asBurkholderia, Megasphaera, Sporolactobacillus, andPropionibacterium (Hernndez-Mendoza et al. 2014a, b,Zaiat et al. 2016), which are related mainly to the productionof other metabolites including volatile fatty acids, ethanol, andlactic acid, has been observed.

The reactor configuration was also an important factor. Thelower solid retention time at CSTR developed a less diverseand more active hydrogen producer biomass. Unlike fixedbiomass reactors (UASB, EGSB, and FBR), that could divertthe available electrons to not hydrogenogenic metabolic path-ways due to the higher species diversity (Fig. 1). The sampletaken from the UASB operated in discontinuous modepresented the higher diversity, but, it cannot be affirmed thatthis difference was due to the discontinuous operation or dueto other factors as substrate, inoculum, or other operational

conditions. However, according to the CCA analysis(Fig. 5c), the samples from the UASB grouped together andthe discontinuous operation mode did not produce a separa-tion of the samples.

The CCA (Fig. 5c) also showed that the microbial commu-nity distribution and its ecological distances was associated toreactor configuration, explained by the selection at differentbiomass retention time in suspended and fixed biomass-basedreactors. The washout pressure due to the low HRT applied toCSTR could explain the lower diverse community comparedto biomass fixed reactors with higher solid retention promptby the bacteria adhesion capability. In this sense, Koskinenet al. (2007) have previously determined that biofilm-basedreactors could enrich hydrogen-consuming microorganisms atmesophilic conditions due to their adhesion capability. In or-der to explain the differences inside sample B, the influence ofthe time of enrichment and the packing material should beanalyzed. Regarding the time of enrichment, it should be not-ed that sample B1 was taken after 90 days of operation whilethe other samples were taken after 60 days of operation so thisis a factor that could explain the higher diversity observed inthis sample and should be studied more deeply in futureworks.

With respect to the influence of packing material used inFBR, it is worth mentioning the high microbial diversityprovided by polyurethane foam matrices (B1Fig. 2) andthe selection of Firmicutes or Proteobacteria provided bylow-density polyethylene (B3 and B4, respectively, Fig. 2).In this case, the selection differed due to the different supple-mentation of calcium in the medium (3.61 mg/L for B3 and2.45 mg/L for B4). Moreover, low-density polyethyleneseems to avoid adhesion of methanogenic microorganismsand polyurethane foam seems to be a good support for suchorganisms (Table 3). These findings are in agreement withSilva et al. (2006), who demonstrated that low-density poly-ethylene was mainly colonized by hydrolytic and fermentativebacteria, thus suggesting its application in acidogenic reactors.Otherwise, polyurethane foam matrices provided a moreequilibrated distribution of methanogenic and acidogenic mi-croorganisms, making this support suitable for application inmethanogenic reactors.

One important factor in hydrogen production is the sub-strate used. It was previously stated that carbohydrates withlonger-length chain decrease the molar yield (Qumneuret al. 2011), explaining the high correlation between microbialcommunities fed with glucose and their volumetric productionrate (Fig. 5b). Regarding glycerol as substrate, different met-abolic pathways for the hydrogen producer Clostridium sp.,which primarily produces 1,3-propandiol and acetate to thedetriment of hydrogen production, have been reported(Akutsu et al. 2009). The high correlation between communi-ties fed with glycerol and the obtained specific hydrogen pro-duction rate (mmol H2/g VS/day) could be explained by the


higher SOLR applied in these reactors, i.e., from 16.650 gCOD/g VS/day, compared with other reactors (Table 1).

A different analysis should be run for real substrates (suchas cheese whey and organic solid wastes) that could carry theirown indigenous microbial loads, diverting the products anddecreasing the hydrogen production, as suggested previouslyfor cheese whey (Stamatelatou et al. 2011). Figure 5b shows aclose ecological distance between reactors fed with rawcheese whey and organic solid wastes, which is explainedby the dominance of microorganism related to Megasphaeragenus with relative abundance from 25 to 75 % (SamplesM12, M13 and U1 to U4, Fig. 3). In this sense, the presenceof the latter genus could be associated to its spoilage nature aswas discussed by Moreno-Andrade et al. (2015), consideringthat FBR fed with raw cheese whey were inoculated withkitchen compost. Comparing reactors fed with cheese whey,it can be assumed that the inoculum was more determinativethan substrate defining the microbial composition since sam-ples U5 and U6 were inoculated with indigenous microorgan-isms and were dominated by families Bifidobacteriaceae andLactobacillaceae. When the microbial communities fromsample C (similar reactor configuration and operated undersimilar conditions) were compared, the substrate was not themain factor that determines the microbial community in thesystem. Li et al. (2010) working with two CSTR operatedwithglucose and starch as substrates under similar conditions ob-served significant differences in the dominant microorgan-isms. Then, not all substrate types have the same influenceon the microbial community.

Despite different reactor configuration, inoculum sources,substrates, and laboratories, clear patterns were observed inthe communities analyzed in the present study. Most of thecommunities were uneven and presented low diversity. Thephylum Firmicutes predominated most reactors. Three phys-iological groups were detected: 1organisms with high hy-drogen yield (as Clostridium, Kosmotoga, Enterobacter), 2fermenters with low hydrogen yield (different genera withinthe family Veillonelaceae), and 3competitors (mainly lacticacid bacteria). Understanding the interaction between thesethree groups of microorganisms could be the clue to managethis kind of reactors.

In the past decade, the research effort was focused on en-hancing the hydrogen production by optimizing the operationparameters, modifying the reactor configuration, using differ-ent inoculum sources and different substrates. Microbial ecol-ogy studies give new insights to understand how differentmicrobial communities affect the overall functionality of anecosystem. The results presented here are a starting point tounderstand the complexity of this kind of ecosystems and theinteraction between the different organisms present in thehydrogen-producing reactor communities. With this informa-tion, it would be possible to design strategies to increase thehydrogen production and stability of this kind of reactors.

Acknowledgments This work was performed in the frame of the Bio-hydrogen Latin America network. The following projects and institutionsfunded this work: ANII project FSE 6437 and FSE 102488 (Uruguay),SEP-CONACYT 240087 (UNAM, Mxico), PAPIIT project IT 100113(DGAPA-UNAM, Mxico), SEP-CONACYT 132483 (IPICYT,Mxico), KBBE-7PM GRAIL 613667 (Chile). L.F. and J.W. are fundedby ANII-Uruguay grants.

Compliance with ethical standards

Ethical approval This article does not contain any studies with humanparticipants or animals performed by any of the authors.

Conflict of interest The authors declare that they have no competinginterests.

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Microbial communities from 20 different hydrogen-producing reactors studied by 454 pyrosequencingAbstractIntroductionMaterials and methodsReactors and samplingDNA extraction and 454 pyrosequencing of 16S rRNA genesAnalysis of 454 pyrosequencing resultsMultivariate and statistical analysis

ResultsClassification of the readsMultivariate analysis results

DiscussionGenera with high capacity for hydrogen productionGenera with low capacity for hydrogen productionGenera that compete with hydrogen-producing organismsHydrogen-consuming organismsInfluence of the inoculum, substrate, reactor configuration, and operational conditions in the microbial communities

References

microbial communities from 20 different hydrogen-producing reactors studied by 454 pyrosequencing

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